Conference Articles¶

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pre-2000¶

[1] A. Engelbrecht, “A model for the estimation of offered traffic from measured traffic parameters,” in Proceedings of the itc sponsored st. petersburg international teletraffic seminar, 1995.

[2] A. Engelbrecht, I. Cloete, J. Geldenhuys, and J. Zurada, “Automatic scaling using gamma learning in feedforward neural networks,” in Proceedings of the international workshop on artificial neural networks, vol. 930, J. Mira and F. Sandoval, Eds. 1995, pp. 374–381.

[3] A. Engelbrecht, I. Cloete, and J. Zurada, “Determining the significance of input parameters using sensitivity analysis,” in Proceedings of the international workshop on artificial neural networks, vol. 930, J. Mira and F. Sandoval, Eds. 1995, pp. 382–388.

[4] A. Engelbrecht and I. Cloete, “Dimensioning of telephone networks using a neural network as traffic distribution approximator,” in Proceedings of the international workshop on the applications of neural networks to telecommunications, J. Alspector, R. Goodman, and T. Brown, Eds. 1995, pp. 72–79.

[5] M. Hattingh, A. Engelbrecht, and I. Cloete, “A routing rule simulator for telephone networks,” in Proceedings of the itc sponsored st. petersburg international teletraffic seminar, 1995, pp. 261–269.

[6] H. Viktor, A. Engelbrecht, and I. Cloete, “Reduction of symbolic rules from neural networks using sensitivity analysis,” in Proceedings of the ieee international joint conference on neural networks, 1995, pp. 1022–1026, doi: https://doi.org/10.1109/IJCNN.1995.488892.

[7] A. Engelbrecht and I. Cloete, “A sensitivity analysis algorithm for pruning feedforward neural networks,” in Proceedings of the ieee international joint conference on neural networks, 1996, vol. 2, pp. 1274–1277, doi: https://doi.org/10.1109/IJCNN.1996.549081.

[8] A. Sevenster and A. Engelbrecht, “GARTNet: A genetic algorithm for routing in telecommunications networks,” in Proceedings of the imacs multiconference on computational engineering in systems applications, symposium on control, optimization and supervision, 1998, vol. 2, pp. 1106–1111.

[9] A. Engelbrecht and I. Cloete, “Feature extraction from feedforward neural networks using sensitivity analys,” in Proceedings of the international conference on advances in systems, signals, control and computers, 1998, vol. 2, pp. 221–225.

[10] A. Engelbrecht and I. Cloete, “Selective learning using sensitivity analysis,” in Proceedings of the ieee international joint conference on neural networks, 1998, pp. 1150–1156, doi: https://doi.org/10.1109/IJCNN.1998.685935.

[11] L. Fletcher, V. Katkovnik, F. Steffens, and A. Engelbrecht, “Optimizing the number of hidden nodes of a feedforward artificial neural network,” in Proceedings of the ieee international joint conference on neural networks, 1998, pp. 1608–1612, doi: https://doi.org/10.1109/IJCNN.1998.686018.

[12] H. Viktor, A. Engelbrecht, and I. Cloete, “Incorporating rule extraction from anns into a cooperative learning environment,” in Proceedings of the neural networks & their applications conference, 1998, pp. 385–391.

[13] A. Adejumo and A. Engelbrecht, “A comparative study of neural network active learning algorithms,” in Proceedings of the international conference on artificial intelligence, V. Bajić and D. Sha, Eds. 1999, pp. 31–35.

[14] E. Basson and A. Engelbrecht, “Approximation of a function and its derivative in feedforward neural networks,” in Proceedings of the ieee international joint conference on neural networks, 1999, doi: https://doi.org/10.1109/IJCNN.1999.831531.

[15] A. Engelbrecht, “A new selective learning algorithm for time series approximation using feedforward neural networks,” in Proceedings of the international conference on artificial intelligence, V. Bajić and D. Sha, Eds. 1999, pp. 29–31.

[16] A. Engelbrecht and I. Cloete, “Incremental learning using sensitivity analysis,” in Proceedings of the ieee international joint conference on neural networks, 1999, doi: https://doi.org/10.1109/IJCNN.1999.831159.

[17] A. Engelbrecht, L. Fletcher, and I. Cloete, “Variance analysis of sensitivity information for pruning multilayer feedforward neural networks,” in Proceedings of the ieee international joint conference on neural networks, 1999, doi: https://doi.org/10.1109/IJCNN.1999.832657.

[18] A. Engelbrecht and H. Viktor, “Rule improvement through decision boundary detection using sensitivity analysis,” in Proceedings of the international working conference on artificial neural networks, vol. 1607, J. Mira and J. Sánchez-Andrés, Eds. 1999, pp. 78–84.

[19] A. Ismail and A. Engelbrecht, “Training product units in feedforward neural networks using particle swarm optimization,” in Proceedings of the international conference on artificial intelligence, V. Bajić and D. Sha, Eds. 1999, pp. 36–40.

[20] D. Rodic and A. Engelbrecht, “A hybrid exhaustive and heuristic rule extraction approach,” in Proceedings of the international conference on artificial intelligence, V. Bajić and D. Sha, Eds. 1999, pp. 25–28.

2000 to 2004¶

[21] A. Engelbrecht, “Data generation using sensitivity analysis,” in Proceedings of the international symposium on computational intelligence, 2000.

[22] A. Ismail and A. Engelbrecht, “Global optimization algorithms for training product unit neural networks,” in Proceedings of the ieee international conference on neural networks, 2000, doi: https://doi.org/10.1109/IJCNN.2000.857826.

[23] S. Rouwhorst and A. Engelbrecht, “Searching the forest: Using decision trees as building blocks for evolutionary search in classification databases,” in Proceedings of the ieee congress on evolutionary computing, 2000, pp. 633–638, doi: https://doi.org/10.1109/CEC.2000.870357.

[24] A. Engelbrecht, “Selective learning for multilayer feedforward neural networks,” in Proceedings of the 6^th international work-conference on artificial and natural neural networks, vol. 2084, J. Mira and A. Prieto, Eds. 2001, pp. 386–393.

[25] R. Brits and A. Engelbrecht, “A cluster approach to incremental learning,” in Proceedings of the ieee international joint conference on neural network, 2001, doi: https://doi.org/10.1109/IJCNN.2001.938474.

[26] F. van den Bergh and A. Engelbrecht, “Using cooperative particle swarm optimization to train product unit neural networks,” in Proceedings of the ieee international joint conference on neural network, 2001, doi: https://doi.org/10.1109/IJCNN.2001.939004.

[27] F. van den Bergh and A. Engelbrecht, “Effects of swarm size on cooperative particle swarm optimizers,” in Proceedings of the genetic and evolutionary computation conference, 2001, pp. 892–899, doi: https://doi.org/10.5555/2955239.2955400.

[28] R. Brits, A. Engelbrecht, and F. van den Bergh, “Niching particle swarm optimizer,” in Proceedings of the asia-pacific conference on simulated evolution and learning, 2002.

[29] R. Brits, A. Engelbrecht, and F. van den Bergh, “Solving systems of unconstrained equations using particle swarm optimization,” in Proceedings of the ieee international conference on systems, man, and cybernetics, 2002, doi: https://doi.org/10.1109/ICSMC.2002.1176019.

[30] A. Ismail and A. Engelbrecht, “Pruning product unit neural networks,” in Proceedings of the ieee international joint conference on neural network, 2002, doi: https://doi.org/10.1109/IJCNN.2002.1005479.

[31] L. Messerschmidt and A. Engelbrecht, “Learning to play games using a pso-based competitive learning approach,” in Proceedings of the asia-pacific conference on simulated evolution and learning, 2002.

[32] M. Omran, A. Salman, and A. Engelbrecht, “Image classification using particle swarm optimization,” in Proceedings of the asia-pacific conference on simulated evolution and learning, 2002.

[33] G. Potgieter and A. Engelbrecht, “Structural optimization of learned polynomial expressions using genetic algorithms,” in Proceedings of the asia-pacific conference on simulated evolution and learning, 2002.

[34] F. van den Bergh and A. Engelbrecht, “A new locally convergent particle swarm optimiser,” in Proceedings of the ieee international conference on systems, man, and cybernetics, 2002, doi: https://doi.org/10.1109/ICSMC.2002.1176018.

[35] R. Brits, A. Engelbrecht, and F. van den Bergh, “Scalability of nichepso,” in Proceedings of the ieee swarm intelligence symposium, 2003, pp. 228–234, doi: https://doi.org/10.1109/SIS.2003.1202273.

[36] E. Dean, A. Engelbrecht, and A. Nicholas, “Computer aided identification of biological specimens using self-organizing maps,” in Proceedings of the proceedings of the fourth international conference on data mining, 2003, vol. 29(14), doi: https://doi.org/10.2495/DATA030551.

[37] N. Franken and A. Engelbrecht, “Comparing pso structures to learn the game of checkers from zero knowledge,” in Proceedings of the ieee congress on evolutionary computation, 2003, pp. 234–241, doi: https://doi.org/10.1109/CEC.2003.1299580.

[38] U. Paquet and A. Engelbrecht, “A new particle swarm optimiser for linearly constrained optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2003, pp. 227–233, doi: https://doi.org/10.1109/CEC.2003.1299579.

[39] U. Paquet and A. Engelbrecht, “Training support vector machines with particle swarms,” in Proceedings of the ieee international joint conference on neural networks, 2003, doi: https://doi.org/10.1109/IJCNN.2003.1223937.

[40] E. Peer, A. Engelbrecht, and F. van den Bergh, “CIRG@UP optibench: A statistically sound framework for benchmarking optimisation algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2003, pp. 2386–2392, doi: https://doi.org/10.1109/CEC.2003.1299386.

[41] E. Peer, F. van den Bergh, and A. Engelbrecht, “Using neighborhoods with the guaranteed convergence pso,” in Proceedings of the ieee swarm intelligence symposium, 2003, pp. 235–242, doi: https://doi.org/10.1109/SIS.2003.1202274.

[42] D. Rodic and A. Engelbrecht, “INDABA - proposal for an intelligent distributed agent based architecture,” in Proceedings of the second international conference on computational intelligence, robotics and autonomous systems, 2003.

[43] D. Rodic and A. Engelbrecht, “Investigation of low cost hybrid three-layer robot architecture,” in Proceedings of the second international conference on computational intelligence, robotics and autonomous systems, 2003.

[44] D. Rodic and A. Engelbrecht, “Investigation into the applicability of social networks as a task allocation tool for multi-robot teams,” in Proceedings of the second international conference on computational intelligence, robotics and autonomous systems, 2003.

[45] D. Rodic and A. Engelbrecht, “Social networks as coordination technique for multi-robot systems,” in Proceedings of the international conference in intelligent systems design and applications, vol. 23, A. Abraham, K. Franke, and M. Köppen, Eds. 2003, pp. 503–513.

[46] D. van der Merwe and A. Engelbrecht, “Data clustering using particle swarm optimizationn,” in Proceedings of the ieee congress on evolutionary computation, 2003, pp. 215–220, doi: https://doi.org/10.1109/CEC.2003.1299577.

[47] N. Franken and A. Engelbrecht, “PSO approaches to co-evolve ipd strategies,” in Proceedings of the ieee congress on evolutionary computation, 2004, doi: https://doi.org/10.1109/CEC.2004.1330879.

[48] L. Schoeman and A. Engelbrecht, “Using vector operations to identify niches for particle swarm optimization,” in Proceedings of the ieee conference on cybernetics and intelligent systems, 2004, doi: https://doi.org/10.1109/ICCIS.2004.1460441.

2005 to 2009¶

[49] A. Edwards, A. Engelbrecht, and N. Franken, “Nonlinear mapping using particle swarm optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2005, doi: https://doi.org/10.1109/CEC.2005.1554699.

[50] A. Engelbrecht, B. Masiye, and G. Pampará, “Niching ability of basic particle swarm optimization algorithms,” in Proceedings of the ieee swarm intelligence symposium, 2005, doi: https://doi.org/10.1109/SIS.2005.1501650.

[51] N. Franken and A. Engelbrecht, “Investigating binary pso parameter influence on the knights cover problems,” in Proceedings of the ieee congress on evolutionary computation, 2005, doi: https://doi.org/10.1109/CEC.2005.1554696.

[52] M. Omran, A. Engelbrecht, and A. Salman, “Using neighborhood topologies with differential evolutions,” in Proceedings of the international conference on computational intelligence and security, 2005.

[53] M. Omran, A. Engelbrecht, and A. Salman, “Self-adaptive differential evolution,” in Proceedings of the international conference on computational and information science, vol. 3801, Y. H. et al, Ed. 2005, pp. 192–199.

[54] M. Omran, A. Engelbrecht, and A. Salman, “Dynamic Clustering using Particle Swarm Optimization with Application in Unsupervised Image Classification,” in Transactions on engineering, computing and technology, 2005, vol. 9, pp. 199–204.

[55] M. Omran, A. Engelbrecht, and A. Salman, “Differential evolution methods for unsupervised image classification,” in Proceedings of the ieee congress on evolutionary computation, 2005, doi: https://doi.org/10.1109/CEC.2005.1554795.

[56] E. Papacostantis, A. Engelbrecht, and N. Franken, “Coevolving probabilistic game playing agents using particle swarm optimization algorithm,” in Proceedings of the ieee symposium on computational intelligence in games, 2005.

[57] G. Pampará, N. Franken, and A. Engelbrecht, “Combining particle swarm optimisation with angle modulation to solve binary problemn,” in Proceedings of the ieee congress on evolutionary computation, 2005, doi: https://doi.org/10.1109/CEC.2005.1554671.

[58] E. Peer, A. Engelbrecht, B. Masiye, and G. Pampará, “CiClops: Computational intelligence collaborative laboratory of pantological softwars,” in Proceedings of the ieee swarm intelligence symposium, 2005.

[59] L. Schoeman and A. Engelbrecht, “A parallel vector-based particle swarm optimizer,” in Proceedings of the international conference on neural networks and genetic algorithms, B. Ribeiro, R. Albrecht, A. Dobnikar, D. Pearson, and N. Steele, Eds. 2005, pp. 268–271.

[60] J. Conradie and A. Engelbrecht, “Training bao game-playing agents using coevolutionary particle swarm optimization,” in Proceedings of the ieee symposium on computational intelligence in games, 2006, doi: https://doi.org/10.1109/CIG.2006.311683.

[61] A. Edwards and A. Engelbrecht, “Comparing optimisation algorithms for nonlinear mapping,” in Proceedings of the ieee congress on evolutionary computation, 2006, doi: https://doi.org/10.1109/CEC.2006.1688379.

[62] A. Engelbrecht, “Particle swarm optimization: Where does it belong?” in Proceedings of the ieee swarm intelligence symposium, 2006.

[63] M. Neethling and A. Engelbrecht, “Determining rna secondary structure using set-based particle swarm optimization,” in Proceedings of the ieee congress on evolutionary computation, 2006, doi: https://doi.org/10.1109/CEC.2006.1688509.

[64] M. Omran, A. Engelbrecht, and A. Salman, “Fully informed differential evolution,” in Proceedings of the international conference on computational intelligence and security, 2006, doi: https://doi.org/10.1109/ICCIAS.2006.294137.

[65] M. Omran, A. Engelbrecht, and A. Salman, “Using the ring neighborhood topology with self-adaptive differential evolution,” in Proceedings of the international conference on nature in computation, vol. 4221, L. Jiao, L. Wang, X. Gao, J. Liu, and F. Wu, Eds. 2006, pp. 976–979.

[66] M. Omran and A. Engelbrecht, “Self-adaptive differential evolution methods for unsupervised image classification,” in Proceedings of the ieee international conference on cybernetics and intelligent systems, 2006, doi: https://doi.org/10.1109/ICCIS.2006.252239.

[67] G. Pampará, A. Engelbrecht, and N. Franken, “Binary differential evolution,” in Proceedings of the ieee congress on evolutionary computation, 2006, doi: https://doi.org/10.1109/CEC.2006.1688535.

[68] L. Schoeman and A. Engelbrecht, “Niching for dynamic environments using particle swarm optimization,” in Proceedings of the asia-pacific conference on simulated evolution and learning, vol. 4247, T. W. et al, Ed. 2006, pp. 134–141.

[69] W. Duminy and A. Engelbrecht, “Tournament particle swarm optimization,” in Proceedings of the ieee symposium on computational intelligence and games, 2007, doi: https://doi.org/10.1109/CIG.2007.368091.

[70] A. Engelbrecht and G. Pampará, “Binary differential evolution strategies,” in Proceedings of the ieee congress on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424711.

[71] A. Engelbrecht and L. van Loggerenberg, “Enhancing the nichepso,” in Proceedings of the ieee congress on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424757.

[72] A. Graaff and A. Engelbrecht, “Local network neighborhood artificial immune system for data clustering,” in Proceedings of the ieee congress cec.2007.4424480on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424480.

[73] J. Grobler and A. Engelbrecht, “A scheduling-specific modeling approach for real world scheduling,” in Proceedings of the ieee international conference on industrial engineering and engineering management, 2007, doi: https://doi.org/10.1109/IEEM.2007.4419156.

[74] J. Grobler, A. Engelbrecht, J. Joubert, and S. Kok, “A starting-time-based approach to production scheduling with particle swarm optimization,” in Proceedings of the ieee symposium on computational intelligence in scheduling, 2007, doi: https://doi.org/10.1109/SCIS.2007.367679.

[75] O. Olorunda and A. Engelbrecht, “Differential evolution in high-dimensional search spaces,” in Proceedings of the ieee congress on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424710.

[76] M. Omran, A. Engelbrecht, and A. Salman, “Self-adaptive barebones differential evolution,” in Proceedings of the ieee congress on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424834.

[77] M. Omran and A. Engelbrecht, “Differential evolution for integer programming problems,” in Proceedings of the ieee congress on evolutionary computation, 2007, doi: https://doi.org/10.1109/CEC.2007.4424749.

[78] M. Omran, A. Engelbrecht, A. Salman, and S. Alsharhan, “Barebones particle swarm for integer programming problems,” in Proceedings of the ieee swarm intelligence symposium, 2007, doi: https://doi.org/10.1109/SIS.2007.368042.

[79] M. Omran, A. Engelbrecht, and A. Salman, “Differential evolution based particle swarm optimization,” in Proceedings of the ieee swarm intelligence symposium, 2007, doi: https://doi.org/10.1109/SIS.2007.368034.

[80] G. Pampará, A. Engelbrecht, and T. Cloete, “CIlib: A collaborative framework for computational intelligence algorithms – part i,” in Proceedings of the ieee international joint conference on neural networks, 2008, doi: https://doi.org/10.1109/IJCNN.2008.4634035.

[81] T. Cloete, A. Engelbrecht, and G. Pampará, “CIlib: A collaborative framework for computational intelligence algorithms – part ii,” in Proceedings of the ieee international joint conference on neural networks, 2008, doi: https://doi.org/10.1109/IJCNN.2008.4634037.

[82] M. du Plessis and A. Engelbrecht, “Improved differential evolution for dynamic optimization problems,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630804.

[83] A. Graaff and A. Engelbrecht, “Towards a self regulating local network neighborhood artificial immune system for data clustering,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630862.

[84] M. Greeff and A. Engelbrecht, “Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4631190.

[85] J. Grobler, A. Engelbrecht, S. Yadavalli, and S. Kok, “Multi-objective de and pso strategies for production scheduling,” in Proceedings of the international federation of operational research societies conference, 2008, doi: https://doi.org/10.1186/s40064-016-3054-z.

[86] J. Grobler, A. Engelbrecht, and S. Yadavalli, “Multi-objective particle swarm optimization for complex job shop scheduling,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630942.

[87] S. Khan and A. Engelbrecht, “A fuzzy ant colony optimization algortihms for topology design of distributed local area networks,” in Proceedings of the ieee swarm intelligence symposium, 2008, doi: https://doi.org/10.1109/SIS.2008.4668303.

[88] J. Lane, A. Engelbrecht, and J. Gain, “Particle swarm optimization with spatially meaningful neighbours,” in Proceedings of the ieee swarm intelligence symposium, 2008, doi: https://doi.org/10.1109/SIS.2008.4668281.

[89] K. Malan and A. Engelbrecht, “Algorithm comparisons and the significance of population size,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630905.

[90] J. Nicholls, A. Engelbrecht, and K. Malan, “Evaluation of fitness functions for evolved stock market forecasting,” in Proceedings of the 7^th international conference on computational intelligence in economics and finance, 2008 [Online]. Available: http://www.aiecon.org/conference/2008/CIEF/Evaluation%20of%20Fitness%20Functions%20for%20Evolved%20Stock%20Market%20Forecasting/s98028571.pdf

[91] O. Olorunda and A. Engelbrecht, “Measuring exploration/exploitation in particle swarms using swarm diversity,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630938.

[92] A. Rakitianskaia and A. Engelbrecht, “Cooperative charged particle swarm optimiser,” in Proceedings of the ieee congress on evolutionary computation, 2008, doi: https://doi.org/10.1109/CEC.2008.4630908.

[93] W. van Heerden and A. Engelbrecht, “A comparison of map neuron labeling approaches for unsupervised self-organizing feature map,” in Proceedings of the ieee international joint conference on neural networks, 2008, doi: https://doi.org/10.1109/IJCNN.2008.4634092.

[94] P. Antoniou, A. Pitsillides, T. Blackwell, and A. Engelbrecht, “Employing the flocking behavior of birds for controlling congestion in autonomous decentralized networks,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983153.

[95] C. Castiello, G. Nitschke, and A. Engelbrecht, “Niche particle swarm optimization for neural network ensembles,” in Proceedings of the european conference on artificial life, vol. 5778, G. Kampis, I. Karsai, and E. Szathmáry, Eds. 2009, pp. 399–407.

[96] A. Engelbrecht, “Finding multiple solutions to unconstrained optimization problems using particle swarm optimization,” in Proceedings of the international conference on mathematical and computational models, 2009.

[97] J. Grobler and A. Engelbrecht, “Hybridizing pso and de for improved vector evaluated multi-objective optimization,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983089.

[98] S. Khan and A. Engelbrecht, “Application of ordered weighted averaging and unified and-or-operators to multi-objective particle swarm optimization algorithm,” in Proceedings of the international conference on fuzzy systems and knowledge discovery, 2009, doi: https://doi.org/10.1109/FSKD.2009.847.

[99] N. Khalid, Z. Ibrahim, T. Kurniawan, M. Khalid, and A. Engelbrecht, “Implementation of binary particle swarm optimization for dna sequence design,” in Proceedings of the international work conference on artifical neural networks, vol. 5518, S. O. et al, Ed. 2009, pp. 450–457.

[100] K. Malan and A. Engelbrecht, “Quantifying ruggedness of continuous landscapes using entropy,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983112.

[101] O. Olorunda and A. Engelbrecht, “An analysis of heterogeneous cooperative algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983128.

[102] M. Omran and A. Engelbrecht, “Free search differential evolution,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4982937.

[103] I. Schoeman and A. Engelbrecht, “Scalability of the vector-based particle swarm optimizer,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983185.

[104] A. Rakitianskaia and A. Engelbrecht, “Training neural networks with pso in dynamic environments,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983009.

[105] M. Riekert, K. Malan, and A. Engelbrecht, “Adaptive genetic programming for dynamic classification problems,” in Proceedings of the ieee congress on evolutionary computation, 2009, doi: https://doi.org/CEC.2009.4983010.

[106] W. van Heerden and A. Engelbrecht, “HybridSOM: A generic rule extraction framework for self-organizing feature mapss,” in Proceedings of the international conference on datamining, 2009, doi: https://doi.org/CIDM.2009.4938624.

2010 to 2014¶

[107] P. Antoniou, A. Pitsillides, A. Engelbrecht, and T. Blackwell, “Mimicking the bird flocking behavior for controlling congestion in sensor networks,” in Proceedings of the third international symposium on applied sciences in biomedical and communication technologies, 2010, doi: https://doi.org/10.1109/ISABEL.2010.5702785.

[108] A. Engelbrecht, “Heterogeneous particle swarm optimization,” in Proceedings of the seventh international conference on swarm intelligence, vol. 6234, M. D. et al, Ed. 2010, pp. 190–202.

[109] J. Grobler, A. Engelbrecht, G. Kendall, and V. Yadavalli, “Alternative hyper-heuristic strategies for multi-method global optimization,” in Proceedings of the ieee congress on evolutionary computation, 2010, doi: https://doi.org/CEC.2010.5585980.

[110] L. Langenhoven, W. van Heerden, and A. Engelbrecht, “Swarm tetris: Applying particle swarm optimization to tetris,” in Proceedings of the ieee congress on evolutionary computation, 2010, doi: https://doi.org/CEC.2010.5586033.

[111] K. Malan and A. Engelbrecht, “Techniques for characterising fitness landscape complexity: How they have evolved and a way forward,” in Proceedings of the international conference on metaheuristics and nature inspired computing, 2010.

[112] I. Schoeman and A. Engelbrecht, “Effect of particle initialization on the performance of particle swarm niching algorithms,” in Proceedings of the seventh international conference on swarm intelligence, vol. 6234, M. D. et al, Ed. 2010, pp. 560–561.

[113] A. van Wyk and A. Engelbrecht, “Overfitting by pso trained feedforward neural networks,” in Proceedings of the ieee congress on evolutionary computation, 2010, doi: https://doi.org/CEC.2010.5586333.

[114] P. Antoniou, A. Pitsillides, A. Engelbrecht, T. Blackwell, and L. Michael, “Applying swarm intelligence to a novel congestion control approach for wireless sensor networks,” in Proceedings of the fourth international symposium on applied sciences in biomedical and communication technologies, 2011, doi: https://doi.org/10.1145/2093698.2093776.

[115] M. du Plessis and A. Engelbrecht, “Self-adaptive competitive differential evolution for dynamic environments,” in Proceedings of the ieee symposium on differential evolution, 2011, doi: https://doi.org/10.1109/SDE.2011.5952054.

[116] A. Dymond, A. Engelbrecht, and S. Heyns, “The sensitivity of single objective optimization algorithm control parameter values under different computational constraints,” in Proceedings of the ieee congress on evolutionary computation, 2011, doi: https://doi.org/CEC.2011.5949781.

[117] A. Engelbrecht, “Scalability of a heterogeneous particle swarm optimizer,” in Proceedings of the ieee swarm intelligence symposium, 2011, doi: https://doi.org/10.1109/SIS.2011.5952563.

[118] J. Grobler, A. Engelbrecht, G. Kendall, and V. Yadavalli, “Investigating the impact of alternative evolutionary selection strategies on multi-method global optimization,” in Proceedings of the ieee congress on evolutionary computation, 2011, doi: https://doi.org/CEC.2011.5949906.

[119] M. Helbig and A. Engelbrecht, “Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2011, doi: https://doi.org/CEC.2011.5949867.

[120] S. Khan and A. Engelbrecht, “Assessment of the ‘evaluation’ function in the simulated evolution algorithm,” in Proceedings of the seventh international conference on natural computation, 2011, doi: https://doi.org/10.1109/ICNC.2011.6022287.

[121] R. Klazar and A. Engelbrecht, “Dynamic load balancing inspired by division of labour in ant colonies,” in Proceedings of the ieee swarm intelligence symposium, 2011, doi: https://doi.org/10.1109/SIS.2011.5952568.

[122] J. Langeveld-van Gendt and A. Engelbrecht, “A generic set-based particle swarm optimization algorithm,” in Proceedings of the international conference on swarm intelligence, 2011.

[123] B. Leonard, A. Engelbrecht, and A. van Wyk, “Heterogeneous particle swarms in dynamic environments,” in Proceedings of the ieee swarm intelligence symposium, 2011, doi: https://doi.org/10.1109/SIS.2011.5952564.

[124] J. Nicholls, K. Malan, and A. Engelbrecht, “Comparison of trade decision strategies in an equity market ga trader,” in Proceedings of the ieee symposium on computational intelligence for financial engineering & economics, 2011, doi: https://doi.org/10.1109/CIFER.2011.5953553.

[125] G. Pampará and A. Engelbrecht, “Binary artificial bee colony optimization,” in Proceedings of the ieee swarm intelligence symposium, 2011, doi: https://doi.org/10.1109/SIS.2011.5952562.

[126] E. Papacostantics and A. Engelbrecht, “Coevolutionary particle swarm optimization for evolving trend reversal indicators,” in Proceedings of the ieee symposium on computational intelligence for financial engineering & economics, 2011, doi: https://doi.org/10.1109/CIFER.2011.5953552.

[127] A. van Wyk and A. Engelbrecht, “Lamda-gamma learning with feedforward neural networks using particle swarm optimization,” in Proceedings of the ieee swarm intelligence symposium, 2011, doi: https://doi.org/10.1109/SIS.2011.5952561.

[128] J. Abbott and A. Engelbrecht, “Performance of bacterial foraging optimization in dynamic environments,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 284–291.

[129] C. Cleghorn and A. Engelbrecht, “Piecewise linear approximation of unknown n-dimensional parametric curves using particle swarms,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo. et al, Ed. 2012, pp. 292–299.

[130] J. Duhain and A. Engelbrecht, “Towards a more complete classification system for dynamically changing environments,” in Proceedings of the ieee congress on evolutionary computation, 2012, doi: https://doi.org/10.1109/CEC.2012.6252881.

[131] A. Engelbrecht, “Particle swarm optimization: Velocity initialization,” in Proceedings of the ieee congress on evolutionary computation, 2012, doi: https://doi.org/10.1109/CEC.2012.6256112.

[132] J. Grobler, A. Engelbrecht, G. Kendall, and S. Yadavalli, “Investigating the use of local search for improving meta-hyper-heuristic performance,” in Proceedings of the ieee congress on evolutionary computation, 2012, doi: https://doi.org/10.1109/CEC.2012.6252970.

[133] M. Helbig and A. Engelbrecht, “Analyses of guide update approaches for vector evaluated particle swarm optimisation on dynamic multi-objective optimisation problems,” in Proceedings of the ieee congress on evolutionary computation, 2012, doi: https://doi.org/10.1109/CEC.2012.6252882.

[134] A. Ismail and A. Engelbrecht, “Self-adaptive particle swarm optimization,” in Proceedings of the international conference on simulated evolution and learning, vol. 7673, L. Bui, Y. Ong, N. Hoai, H. Ishibuchi, and P. Suganthan, Eds. 2012, pp. 228–237.

[135] A. Ismail and A. Engelbrecht, “Measuring diversity in the cooperative particle swarm optimizer,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 97–108.

[136] A. Ismail and A. Engelbrecht, “The self-adaptive comprehensive learning particle swarm optimize,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 156–167.

[137] R. Klazar and A. Engelbrecht, “Dynamic load balancing inspired by cemetery formation in ant colonies,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 236–243.

[138] B. Leonard and A. Engelbrecht, “Scalability study of particle swarm optimizers in dynamic environments,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 121–132.

[139] F. Nepomuceno and A. Engelbrecht, “A self-adaptive heterogeneous pso inspired by ants,” in Proceedings of the international swarm intelligence conference, vol. 7461, M. Dorigo et al, Ed. 2012, pp. 188–195.

[140] W. van Heerden and A. Engelbrecht, “Unsupervised weight-based cluster labeling for self-organizing maps,” in Proceedings of the workshop on self-organizing maps, advances in self-organizing maps. advances in intelligent systems and computing, 2012, vol. 198, pp. 45–54, doi: https://doi.org/10.1007/978-3-642-35230-0_5.

[141] A. Engelbrecht, “Particle Swarm Optimization: Iteration Strategies Revisited,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.30.

[142] A. Engelbrecht, “Particle Swarm Optimization: Global Best or Local Best?” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.31.

[143] A. Engelbrecht, “Roaming Behavior of Unconstrained Particles,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.28.

[144] A. Engelbrecht, “Particle Swarm Optimization with Discrete Crossover,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557864.

[145] A. Engelbrecht, “Fruitless Search in Differential Evolution,” in Proceedings of the ieee symposium on differential evolution, ieee symposium series on computational intelligence, 2013, doi: https://doi.org/10.1109/SDE.2013.6601436.

[146] K. Georgiva and A. Engelbrecht, “A Cooperative Multi-population Approach to Clustering Temporal Data,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557802.

[147] K. Georgiva and A. Engelbrecht, “Dynamic Differential Evolution Algorithm for Clustering Temporal Data,” in Proceedings of the international conference on large-scale scientific computing, vol. 8353, I. Lirkov, S. Margenov, and J. Waniewski, Eds. Springer, 2013, pp. 240–247.

[148] J. Grobler and A. Engelbrecht, “Solution Space Diversity Management in A Meta-Hyperheuristic Framework,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.51.

[149] J. Grobler, A. Engelbrecht, G. Kendall, and V. Yadavalli, “Multi-Method Algorithms: Investigating the Entity-Algorithm Allocation Problem,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557619.

[150] K. Harrison, A. Engelbrecht, and B. Ombuki-Berman, “A Scalability Study of Multi-Objective Particle Swarm Optimizers,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557570.

[151] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “Knowledge Transfer Strategies for Vector Evaluated Particle Swarm Optimization,” in Proceedings of the international conference on evolutionary multi-criterion optimization, vol. 7811, Springer, 2013, pp. 171–184.

[152] M. Helbig and A. Engelbrecht, “Challenges of Dynamic Multi-objective Optimisation,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.49.

[153] M. Helbig and A. Engelbrecht, “Analysing the Performance of Dynamic Multi-Objective Optimisation Algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557744.

[154] M. Helbig and A. Engelbrecht, “Issues with Performance Measures for Dynamic Multi-objective Optimisation,” in Proceedings of the ieee symposium on computational intelligence in dynamic and uncertain environments, ieee symposium series on computational intelligence, 2013, doi: https://doi.org/10.1109/CIDUE.2013.6595767.

[155] M. Helbig and A. Engelbrecht, “Benchmarks for Dynamic Multi-objective Optimisation,” in Proceedings of the ieee symposium on computational intelligence in dynamic and uncertain environments, ieee symposium series on computational intelligence, 2013, doi: https://doi.org/10.1109/CIDUE.2013.6595776.

[156] B. Leonard and A. Engelbrecht, “On the Optimality of Particle Swarm Parameters in Dynamic Environments,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557748.

[157] B. Malan and A. Engelbrecht, “Ruggedness, Funnels and Gradients in Fitness Landscapes and the Effect on PSO Performance,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557671.

[158] B. Malan and A. Engelbrecht, “Steep Gradients as a Predictor of PSO Failure,” in Proceedings of the 15^th annual conference companion on genetic and evolutionary computation, 2013, doi: https://doi.org/10.1145/2464576.2464582.

[159] W. Matthysen, A. Engelbrecht, and K. Malan, “Analysis of Stagnation Behavior of Vector Evaluated Particle Swarm Optimisation,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2013, doi: https://doi.org/10.1109/SIS.2013.6615173.

[160] J. Mwaura, E. Keedwell, and A. Engelbrecht, “Evolved Linker Gene Expression Programming: A New Technique for Symbolic Regression,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.22.

[161] F. Nepomuceno and A. Engelbrecht, “Behavior Changing Schedules for Heterogeneous Particle Swarms,” in Proceedings of the brics congress on computational intelligence & brazillian congress on computational intelligence, 2013, doi: https://doi.org/10.1109/BRICS-CCI-CBIC.2013.29.

[162] F. Nepomuceno and A. Engelbrecht, “A self-adaptive heterogeneous PSO for real parameter optimization,” in Proceedings of the ieee congress on evolutionary computation, 2013, doi: https://doi.org/10.1109/CEC.2013.6557592.

[163] N. Unger, B. Ombuki-Berman, and A. Engelbrecht, “Cooperative Particle Swarm Optimization in Dynamic Environments,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2013, doi: https://doi.org/10.1109/SIS.2013.6615175.

[164] J. Abbott and A. Engelbrecht, “Nature-inspired Swarm Robotics Algorithms for Prioritized Foraging,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 8667, M. Dorigo et al, Ed. Springer, 2014, pp. 246–253.

[165] P. Bosman and A. Engelbrecht, “Diversity Rate of Change Measurement for Particle Swarm Optimisers,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 8667, M. Dorigo et al, Ed. Springer, 2014, pp. 86–97.

[166] C. Cleghorn and A. Engelbrecht, “Particle Swarm Convergence: Standardized Analysis and Topological Influence,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 8667, M. Dorigo et al, Ed. Springer, 2014, pp. 134–145.

[167] C. Cleghorn and A. Engelbrecht, “A Generalized Theoretical Deterministic Particle Swarm Model,” in Hot off the press worshop of genetic and evolutionary computation conference, 2014.

[168] C. Cleghorn and A. Engelbrecht, “Particle Swarm Convergence: An Empirical Investigation,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/ 10.1109/CEC.2014.6900439.

[169] R. Garden and A. Engelbrecht, “Analysis and Classification of Function Optimisation Benchmark Function and Benchmark Suites,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/ 10.1109/CEC.2014.6900240.

[170] K. Georgieva and A. Engelbrecht, “Cooperative DynDE for Temporal Data Clustering,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/ 10.1109/CEC.2014.6900344.

[171] J. Grobler and A. Engelbrecht, “The Entity-to-Algorithm Allocation Problem: Extending the Analysis,” in Proceedings of the ieee symposium on computational intelligence in ensemble learning, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/CIEL.2014.7015744.

[172] J. Grobler, A. Engelbrecht, G. Kendall, and V. Yadavalli, “Heuristic Space Diversity Management in a Meta-Hyperheuristic Framework,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/ 10.1109/CEC.2014.6900263.

[173] A. Engelbrecht, “Asynchronous Particle Swarm Optimization with Discrete Crossover,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011788.

[174] A. Engelbrecht, “Fitness Function Evaluations: A Fair Stopping Condition?” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011793.

[175] K. Harrison, B. Ombuki-Bernman, and A. Engelbrecht, “Dynamic Multi-Objective Optimization using Charged Vector Evaluated Particle Swarm Optimization,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900399.

[176] M. Helbig and A. Engelbrect, “Using Heterogeneous Knowledge Sharing Strategies with Dynamic Vector-evaluated Particle Swarm Optimisation,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011804.

[177] M. Helbig and A. Engelbrecht, “Heterogeneous Dynamic Vector Evaluated Particle Swarm Optimisation for Dynamic Multi-Objective Optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900303.

[178] R. Klazar and A. Engelbrecht, “Parameter Optimization by Means of Statistical Quality Guides in F-Race,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900446.

[179] B. Leonard and A. Engelbrecht, “Angle Modulated Particle Swarm Variants,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 8667, M. D. et al, Ed. Springer, 2014, pp. 38–49.

[180] K. Malan and A. Engelbrecht, “Particle Swarm Optimisation Failure Prediction Based on Fitness Landscape Characteristics,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011789.

[181] K. Malan and A. Engelbrecht, “A Progressive Random Walk Algorithm for Sampling Continuous Fitness Landscapes,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900576.

[182] A. Rakitianskaia and A. Engelbrecht, “Weight Regularisation in Particle Swarm Optimisation Neural Network Training,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011773.

[183] A. Rakitianskaia and A. Engelbrecht, “Training High-Dimensional Neural Networks with Cooperative Particle Swarm Optimiser,” in Proceedings of the international joint conference on neural networks, 2014, doi: https://doi.org/10.1109/IJCNN.2014.6889933.

[184] S. Reid, K. Malan, and A. Engelbrecht, “Carry Trade Portfolio Optimization using Particle Swarm Optimization,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900497.

[185] C. Scheepers and A. Engelbrecht, “Analysis of Stagnation Behaviour of Competitive Coevolutionary Trained Neuro-Controllers,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011795.

[186] C. Scheepers and A. Engelbrecht, “Competitive Coevolutionary Training of Simple Soccer Agents from Zero Knowledge,” in Proceedings of the ieee congress on evolutionary computation, 2014, doi: https://doi.org/10.1109/CEC.2014.6900236.

[187] C. Stallmann and A. Engelbrecht, “A Comparison of Interpolation Algorithms for Gramaphone Record Sound Restoration,” in Proceedings of the international conference on signal processing and integrated networks, 2014, doi: https://doi.org/10.1109/SPIN.2014.6776913.

[188] S. van der Stockt and A. Engelbrecht, “Analysis of Hyper-heuristic Performance in Different Dynamic Environments,” in Proceedings of the ieee symposium on computational intelligence in dynamic and uncertain environments, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/CIDUE.2014.7007860.

[189] E. van Zyl and A. Engelbrecht, “Comparison of Self-Adaptive Particle Swarm Optimizers,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2014, doi: https://doi.org/10.1109/SIS.2014.7011775.

2015 to 2019¶

[190] N. Banda, A. Engelbrecht, and P. Robinson, “Feature Reduction for Dimensional Emotion Recognition in Human-Robot Interaction,” in Proceedings of the ieee symposium on computational intelligence in healthcare and e-health, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.119.

[191] N. Banda, A. Engelbrecht, and P. Robinson, “Continuous Emotion Recognition using a Particle Swarm Optimized NARX Neural Network,” in Proceedings of the 6^th international conference on affective computing and intelligent interaction, 2015, doi: https://doi.org/10.1109/ACII.2015.7344599.

[192] R. Bond, A. Engelbrecht, and B. Ombuki-Bernman, “Evaluating Landscape Characteristics of Dynamic Benchmark Functions,” in Proceedings of the ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7257044.

[193] C. Cleghorn and A. Engelbrecht, “Fully Informed Particle Swarm Optimizer: Convergence Analysis,” in Proceedings of th eIEEE congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7256888.

[194] D. Dibblee, J. Maltese, B. Ombuki-Berman, and A. Engelbrecht, “Vector-Evaluated Particle Swarm Optimization with Local Search,” in Proceedings of the ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7256891.

[195] M. du Plessis, A. Engelbrecht, and A. Calitz, “Self-Adapting the Brownian Radius in a Differential Evolution Algorithm for Dynamic Environments,” in Proceedings of the acm conference on foundations of genetic algorithms xiii, 2015, pp. 114–128, doi: https://doi.org/10.1145/2725494.2725505.

[196] J. Grobler and A. Engelbrecht, “Metric-based Heuristic Space Diversity Management in a Meta-heuristic Framework,” in Proceedings of the ieee symposium on computational intelligence and ensemble learning, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.234.

[197] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.44.

[198] M. Helbig and A. Engelbrecht, “Using Headless-chicken Crossover for Local Guide Selection when Solving Dynamic Multi-Objective Optimization,” in Proceedings of the conference on advances in nature and biologically inspired computing, vol. 419, N. Pillay, A. Engelbrecht, A. Abraham, M. du Plessis, V. Snášel, and A. Muda, Eds. Springer, 2015, pp. 381–392.

[199] M. Helbig and A. Engelbrecht, “Dynamic Vector-evaluated PSO with Guaranteed Convergence in the Sub-swarms,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.184.

[200] M. Helbig and A. Engelbrecht, “Influence of the Archive Size on the Performance of the Dynamic Vector Evaluated Particle Swarm Optimisation Algorithm Solving Dynamic Multi-objective Optimisation Problems,” in Proceedings of the ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7257121.

[201] M. Helbig and A. Engelbrecht, “The Effect of Quantum and Charged Particles on the Performance of the Dynamic Vector-evaluated Particle Swarm Optimisation Algorithm Solving Dynamic Multi-objective Optimisation Problems,” in Proceedings of the annual conference on genetic and evolutionary computation, 2015, doi: https://doi.org/10.1145/2739480.2754810.

[202] B. Leonard and A. Engelbrecht, “Frequency Distribution of Candidate Solutions in Angle Modulated Particle Swarms,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.45.

[203] J. Maltese, B. Ombuki-Berman, and A. Engelbrecht, “Co-operative Vector-evaluated Particle Swarm Optimization,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.185.

[204] J. Maltese, A. Engelbrecht, and B. Ombuki-Bernman, “High-Dimensional Multi-Objective Optimization using Co-operative Vector-Evaluated Particle Swarm Optimization with Random Variable Grouping,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.186.

[205] K. Malan, J. Oberholzer, and A. Engelbrecht, “Characterising Constrained Continuous Optimisation Problems,” in Proceedings of the ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7257045.

[206] G. Pampara and A. Engelbrecht, “Towards A Generic Computational Intelligence Library: Preventing Insanity,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.207.

[207] A. Rakitianskaia and A. Engelbrecht, “Measuring Saturation in Neural Networks,” in IEEE symposium on foundations of computational intelligence, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.202.

[208] A. Rakitianskaia and A. Engelbrecht, “Saturation in PSO Neural Network Training: Good or Evil?” in Proceedings of te ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7256883.

[209] C. Stallmann and A. Engelbrecht, “Gramophone Noise Reconstruction: A Comparative Study of Interpolation Algorithms for Noise Reduction,” in Proceedings of the 12^th international joint conference on e-business and telecommunications, 2015 [Online]. Available: https://ieeexplore.ieee.org/document/7518105

[210] E. van Zyl and A. Engelbrecht, “A Subspace-based Method for PSO Initialization,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2015, doi: https://doi.org/10.1109/SSCI.2015.42.

[211] S. van der Stockt and A. Engelbrecht, “Analysis of Global Information Sharing in Hyper-heuristics for Different Dynamic Environments,” in Proceedings of the ieee congress on evolutionary computation, 2015, doi: https://doi.org/10.1109/CEC.2015.7256976.

[212] A. Bosman, A. Engelbrecht, and M. Helbig, “Search Space Boundaries in Neural Network Error Landscape Analysis,” in Proceedings of the ieee symposium on foundations of computational intelligence, ieee symposium series on computational intelligence, 2016, doi: https://doi.org/10.1109/SSCI.2016.7850152.

[213] C. Cleghorn and A. Engelbrecht, “Particle Swarm Optimizer: The Impact of Unstable Particles on Performance,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2016, doi: https://doi.org/10.1109/SSCI.2016.7850265.

[214] C. Cleghorn and A. Engelbrecht, “Unified Particle Swarm Optimizer: Convergence Analysis,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7743828.

[215] D. Doman, M. Helbig, and A. Engelbrecht, “Heterogeneous Vector-Evaluated Particle Swarm Optimisation in Static Environments,” in Proceedings of the seventh international conference on swarm intelligence, advances in swarm intelligence, 2016, vol. 9712, pp. 293–304, doi: https://doi.org/10.1007/978-3-319-41000-5_29.

[216] J. Grobler and A. Engelbrecht, “Headless Chicken Particle Swarm Optimization Algorithm,” in Proceedings of the seventh international conference on swarm intelligence, advances in swarm intelligence, vol. 9712, Y. Tan, Y. Shi, and B. Niu, Eds. Springer, 2016, pp. 350–357.

[217] J. Grobler and A. Engelbrecht, “Headless Chicken Particle Swarm Optimization Algorithms for Improved Diversity in a Dynamically Changing Environment,” in Proceedings of the 3^rd international conference on soft computing & machine intelligence, 2016, doi: https://doi.org/10.1109/ISCMI.2016.45.

[218] J. Grobler and A. Engelbrecht, “Hyper-heuristics for the Flexible Job Shop Scheduling Problem with Additional Constraints,” in Proceedings of the conference on swarm intelligence, advances in swarm intelligence, vol. 9713, Y. Tan, Y. sho, and L. Li, Eds. Springer, 2016, pp. 3–10.

[219] K. Harrison, A. Engelbrecht, and B. Ombuki-Bernman, “The Sad State of Self-Adaptive Particle Swarm Optimizers,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7743826.

[220] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “A Radius-Free Quantum Particle Swarm Optimization Technique for Dynamic Optimization Problems,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7743845.

[221] M. Helbig, K. Deb, and A. Engelbrecht, “Key Challenges and Future Directions of Dynamic Multi-objective Optimisation,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7743931.

[222] R. Koen, A. Engelbrecht, and E. Pretorius, “A Best-Effort Fibrin Strand Detection Algorithm,” in Proceedings of the 3^rd international conference on soft computing & machine intelligence, 2016, doi: https://doi.org/10.1109/ISCMI.2016.25.

[223] J. Maltese, B. Ombuki-Berman, and A. Engelbrecht, “Pareto-Based Many-Objective Optimization using Knee Points,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7744255.

[224] J. Mwaura, A. Engelbrecht, and F. Nepomuceno, “Performance Measures for Niching Algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7744401.

[225] A. Rakitianskaia, E. Bekker, K. Malan, and A. Engelbrecht, “Analysis of Error Landscapes in Multi-layerd Neural Nertworks for Classification,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7748360.

[226] C. Scheepers and A. Engelbrecht, “Vector Evaluated Particle Swarm Optimization Archive Management: Pareto Optimal Front Diversity Sensitivity Analysis,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2016, doi: https://doi.org/10.1109/SSCI.2016.7850264.

[227] C. Scheepers and A. Engelbrecht, “Misleading Metrics for Pareto Optimal Front Diversity: Spacing and Distribution,” in Proceedings of the ieee multicriteria decision making, ieee symposium series on computational intelligence, 2016, doi: https://doi.org/10.1109/SSCI.2016.7850218.

[228] C. Scheepers and A. Engelbrecht, “Vector Evaluated Particle Swarm Optimization Part I: Explorative Analysis,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7744013.

[229] C. Scheepers and A. Engelbrecht, “Vector Evaluated Particle Swarm Optimization Part II: Quantitative Analysis,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: http://dx.doi.org/10.1109/CEC.2016.7744014.

[230] W. van Heerden and A. Engelbrecht, “An Investigation into the Effect of Unlabeled Neurons on Self-Organizing Maps,” in IEEE symposium on computational intelligence in data mining, ieee symposium series on computational intelligence, 2016, doi: https://doi.org/10.1109/SSCI.2016.7849938.

[231] A. van Wyk and A. Engelbrecht, “Analysis of Activation Functions for Particle Swarm Optimised Feedforward Neural Networks,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7743825.

[232] E. van Zyl and A. Engelbrecht, “Group-Based Stochastic Scaling for PSO Velocities,” in Proceedings of the ieee congress on evolutionary computation, 2016, doi: https://doi.org/10.1109/CEC.2016.7744015.

[233] A. Volschenk and A. Engelbrecht, “An Analysis of Competitive Coevolutionary Particle Swarm Optimizers to Train Neural Network Game Tree Evaluation Functions,” in Proceedings of the seventh international conference on swarm intelligence, advances in swarm intelligence, vol. 9712, Y. Tan, Y. Shi, and B. Niu, Eds. Springer, 2016, pp. 369–380.

[234] N. Banda and A. Engelbrecht, “Quality Assessment of Large Scale Dimensionality Reduction Methods,” in Proceedings of the ieee 4^th international conference on soft computing and machine intelligence, 2017, doi: https://doi.org/10.1109/ISCMI.2017.8279588.

[235] N. Banda, L. He, and A. Engelbrecht, “Bio-Acoustic Emotion Recognition using Continuous Conditional Recurrent Neural Fields,” in Proceedings of the ieee computational intelligence for human-like intelligence symposium, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/SSCI.2017.8285431.

[236] C. Cleghorn and A. Engelbrecht, “Firefly Optimization: A Study of Frame Invariance,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/SSCI.2017.8285251.

[237] C. Cleghorn and A. Engelbrecht, “Fitness-Distance-Ratio Particle Swarm Optimization: Stability Analysis,” in Proceedings of the genetic and evolutionary computation conference, worksop on fitness landscape analysis, 2017, doi: https://doi.org/10.1145/3071178.3071256.

[238] J. Cronje and A. Engelbrecht, “Training Convolutional Neural Networks with Class Based Data Augmentation for Detecting Distracted Drivers,” in Proceedings of the 9^th international conference on computer and automation engineering, 2017, pp. 126–130, doi: https://doi.org/10.1145/3057039.3057070.

[239] A. Engelbrecht, “Inertia Weight Control Strategies: Particle Roaming Behavior,” in Proceedings of the ieee 4^th international conference on soft computing and machine intelligence, 2017, doi: https://doi.org/10.1109/ISCMI.2017.8279625.

[240] J. Grobler and A. Engelbrecht, “A Scalability Analysis of Partile Swarm Optimization Behaviour,” in Proceedings of the eight international conference on swarm intelligence, vol. 10385, Y. Tan, H. Takagi, and Y. Shi, Eds. Springer, 2017, pp. 119–130.

[241] K. Harrison, A. Engelbrecht, and B. Ombuki-Bernman, “An Adaptive Particle Swarm Optimization Algorithm Based on Optimal Parameter Regions,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/SSCI.2017.8285342.

[242] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “Optimal Parameter Regions for Particle Swarm Optimization Algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2017, doi: https://doi.org/10.1109/CEC.2017.7969333.

[243] R. Koen and A. Engelbrecht, “Maze Exploration using a Fungal Search Algorithm: Part 1 - Empirical Analysis,” in Proceedings of the international conference on intelligent systems, metaheuristics & swarm intelligence, 2017, pp. 40–45, doi: https://doi.org/10.1145/3059336.3059365.

[244] R. Koen and A. Engelbrecht, “Maze Exploration using a Fungal Search Algorithm: Part 2 - Algorithm Model,” in Proceedings of the international conference on intelligent systems, metaheuristics & swarm intelligence, 2017, pp. 46–50, doi: https://doi.org/10.1145/3059336.3059366.

[245] E. Oldewage, A. Engelbrecht, and C. Cleghorn, “The Merits of Velocity Clamping Particle Swarm Optimisation in High Dimensional Spaces,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/SSCI.2017.8280887.

[246] C. Scheepers and A. Engelbrecht, “Quantified Pareto-Optimal Front Comparisons using Attainment Surfaces,” in Proceedings of the ieee sympusium on multicriteria decision making, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/SSCI.2017.8280864.

[247] C. Scheepers and A. Engelbrecht, “Vector Evaluated Particle Swarm Optimization: The Archive’s Influence on Performance,” in Proceedings of the ieee sympusium on multicriteria decision making, ieee symposium series on computational intelligence, 2017, doi: https://doi.org/10.1109/CEC.2017.7969361.

[248] N. Banda and A. Engelbrecht, “Multimodal Emotion Recognition using Deep Continuous Conditional Recurrent Neural Fields,” in Proceedings of the internatinal joint conference on neural networks, 2018, pp. 408–413, doi: https://doi.org/10.1109/ijcnn.2018.8489610.

[249] C. Cleghorn, C. Scheepers, and A. Engelbrecht, “Stability Analysis of the Multi-objective Multi-guided particle Swarm Optimizer,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 11172, M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, and V. Trianni, Eds. Springer, 2018, pp. 201–212.

[250] A. Bosman, A. Engelbrecht, and M. Helbig, “Progressive Gradient Walk for Neural Network Fitness Landscape Analysis,” in Proceedings of the genetic and evolutionary computation conference, worksop on fitness landscape analysis, 2018, doi: https://doi.org/10.1145/3205651.3208247.

[251] J. Douglas, A. Engelbrecht, and B. Ombuki-Berman, “Merging and Decomposition Variants of Cooperative Particle Swarm Optimization: New Algorithms for Large Scale Optimization Problems,” in Proceedings of the international conference on intelligent systems, metaheuristics & swarm intelligence, 2018, pp. 70–77, doi: https://doi.org/10.1145/3206185.3206199.

[252] A. Engelbrecht, V. Pauthe, and B. Csakany, “Swarm-Inspired Algorithms for Prioritized Foraging,” in Proceedings of the international conference on intelligent systems, metaheuristics & swarm intelligence, 2018, pp. 48–53, doi: https://doi.org/10.1145/3206185.3206191.

[253] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “Gaussian-Valued Particle Swarm Optimization,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 11172, M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, and V. Trianni, Eds. Springer, 2018, pp. 368–377.

[254] W. Mostert, K. Malan, and A. Engelbrecht, “Filter versus Wrapper Feature Selection based on Problem Landscape Features,” in Proceedings of the genetic and evolutionary computation conference, worksop on fitness landscape analysis, 2018, pp. 1489–1496, doi: https://doi.org/10.1145/3205651.3208305.

[255] E. Oldewage, A. Engelbrecht, and C. Cleghorn, “Boundary Constraint Handling Techniques for Particle Swarm Optimization in High Dimensional Problem Spaces,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 11172, M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, and V. Trianni, Eds. ACM, 2018, pp. 333–341.

[256] E. Oldewage, A. Engelbrecht, and C. Cleghorn, “The Importance of Component-wise Stochasticity in Particle Swarm Optimization,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 11172, M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, and V. Trianni, Eds. ACM, 2018, pp. 264–276.

[257] G. Pampara and A. Engelbrecht, “Self-adaptive Quantum Particle Swarm Optimization for Dynamic Environments,” in Proceedings of the 11^th international swarm intelligence conference (ants), vol. 11172, M. Dorigo, M. Birattari, C. Blum, A. Christensen, A. Reina, and V. Trianni, Eds. ACM, 2018, pp. 163–175.

[258] K. Erwin and A. Engelbrecht, “Control Parameter Sensitivity Analysis of the Multi-guide Particle Swarm Optimization Algorithms,” in Proceedings of the genetic and evolutionary computation conference, evosoft workshop, 2019, doi: https://doi.org/10.1145/3321707.3321739.

[259] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “The Parameter Configuration Landscape: A Case Study on Particle Swarm Optimization,” in Proceedings of the ieee congress on evolutionary computation, 2019, doi: https://doi.org/10.1109/CEC.2019.8790242.

[260] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “An Analysis of Control Parameter Importance in the Particle Swarm Optimization Algorithm,” in Proceedings of the internatinal conference on swarm intelligence, part i, vol. 11655, Y. Tan, Y. Shi, and B. Niu, Eds. Springer, 2019, pp. 93–105.

[261] R. Lang and A. Engelbrecht, “On the Robustness of Random Walks for Fitness Landscape Analysis,” in Proceedings of the ieee symposium on foundations of computational intelligence, ieee symposium series on computational intelligence, 2019.

[262] W. Mostert, K. Malan, G. Ochoa, and A. Engelbrecht, “Insights into the Feature Selection Problem using Local Optima Networks,” in Proceedings of the european conference on evolutionary computation in combinatorial optimization, vol. 11452, A. Liefooghe and L. Paquete, Eds. Springer, 2019, pp. 147–162.

[263] G. Pampara and A. Engelbrecht, “Evolutionary and Swarm-Intelligence Algorithms through Monadic Composition,” in Proceedings of the genetic and evolutionary computation conference, evosoft workshop, 2019, doi: https://doi.org/10.1145/3319619.3326845.

[264] G. Pampara and A. Engelbrecht, “Generator for Dynamically Constrained Optimization Problems,” in Proceedings of the genetic and evolutionary computation conference, evolutionary algorithms for uncertainty workshop, 2019, doi: https://doi.org/10.1145/3319619.3326798.

2020¶

[265] A. Bosman, A. Engelbrecht, and M. Helbig, “Loss Surface Modality of Feed-Forward Neural Network Architectures,” in Proceedings of the ieee international joint conference on neural networks, 2020, doi: https://doi.org/10.1109/IJCNN48605.2020.9206727.

[266] T. Carolus and A. Engelbrecht, “Control Parameter Importance and Sensitivity Analysis of the Multi-Guide Particle Swarm Optimization Algorithm,” in Proceedings of the 12^th international swarm intelligence conference (ants), vol. 12421, M. D. et al, Ed. Springer, 2020, pp. 96–106.

[267] H. Cilliers and A. Engelbrecht, “Fitting Gaussian Mixture Models Using Cooperative Particle Swarm Optimization,” in Proceedings of the 12^th international swarm intelligence conference (ants), vol. 12421, M. D. et al, Ed. Springer, 2020, pp. 298–305.

[268] T. Crane, B. Ombuki-Berman, and A. Engelbrecht, “NichePSO and the Merging Subswarm Problem,” in Proceedings of the conference on soft computing & machine intelligence, 2020, doi: https://doi.org/10.1109/ISCMI51676.2020.9311551.

[269] S. Dennis and A. Engelbrecht, “A Review and Empirical Analysis of Particle Swarm Optimization Algorithms for Dynamic Multi-Modal Optimization,” in Proceedings of the ieee congress on evolutionary computation, 2020, doi: https://doi.org/10.1109/CEC48606.2020.9185587.

[270] S. Dennis and A. Engelbrecht, “Dynamic Multi-Swarm Fractional-best Particle Swarm Optimization for Dynamic Multi-modal Optimization,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2020, doi: https://doi.org/10.1109/SSCI47803.2020.9308350.

[271] A. Engelbrecht and M. Helbig, “Empirical Analysis of A Partial Dominance Approach to Many-Objective Optimisation,” in Proceedings of the 4^th international conference on intelligent systems, metaheuristics & swarm intelligence, 2020, doi: https://doi.org/10.1145/3396474.3396483.

[272] K. Erwin and A. Engelbrecht, “Diversity Measures for Set-based Meta-Heuristics,” in Proceedings of the conference on soft computing & machine intelligence, 2020, doi: https://doi.org/10.1109/ISCMI51676.2020.9311572.

[273] K. Erwin and A. Engelbrecht, “Set-based Particle Swarm Optimization for Portfolio Optimization,” in Proceedings of the 12^th international swarm intelligence conference (ants), vol. 12421, M. D. et al, Ed. Springer, 2020, pp. 333–339.

[274] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “Visualiing and Characterizing the Parameter Configuration Landscape of Differential Evolution using Physical Landform Classification,” in Proceedings of the ieee symposium on foundations of computational intelligence, ieee symposium series on computational intelligence, 2020, doi: https://doi.org/10.1109/SSCI47803.2020.9308536.

[275] M. Helbig and A. Engelbrecht, “Partial Dominance for Many-Objective Optimization,” in Proceedings of the 4^th international conference on intelligent systems, metaheuristics & swarm intelligence, 2020, doi: https://doi.org/10.1145/3396474.3396482.

[276] A. Kamilaris, A. Engelbrecht, A. Pitsillides, and F. Prenafeta-Bold, “Transfer of Manure from Livestock Farms to Crop Fields as Fertilizer using an Ant Inspired Approach,” in Proceedings of the xxiv isprs congress, international society for photogrammetry and remote sensing, 2020, doi: https://doi.org/10.5194/ISPRS-ANNALS-V-4-2020-139-2020.

[277] R. Lang and A. Engelbrecht, “Distributed Random Walks for Fitness Landscape Analysis,” in Proceedings of the genetic and evolutionary computation conference, 2020, doi: https://doi.org/10.1145/3377930.3389829.

[278] R. Lang and A. Engelbrecht, “Decision Space Coverage of Random Walks,” in Proceedings of the ieee congress on evolutionary computation, 2020, doi: https://doi.org/10.1109/CEC48606.2020.9185623.

[279] R. McLean, B. Ombuki-Berman, and A. Engelbrecht, “Swarm-based Algorithms for Neural Network Training,” in Proceedings of the ieee conference on systems, man, and cybernetics, 2020, doi: https://doi.org/10.1109/SMC42975.2020.9283242.

[280] C. Parton and A. Engelbrecht, “Mixtures of Heterogeneous Experts,” in Proceedings of the 4^th international conference on intelligent systems, metaheuristics & swarm intelligence, 2020, doi: https://doi.org/10.1145/3396474.3396484.

[281] T. Schmidt-Dumont and A. Engelbrecht, “Analysis of Particle Swarm Optimisation for Training Support Vector Machines,” in Proceedings of the ieee swarm intelligence symposium, ieee symposium series on computational intelligence, 2020, doi: https://doi.org/10.1109/SSCI47803.2020.9308144.

[282] S. van der Stockt, A. Engelbrecht, and C. Cleghorn, “Heuristic Space Behavior Measures for Population-based Hyper-heuristics,” in Proceedings of the ieee congress on evolutionary computation, 2020, doi: https://doi.org/10.1109/CEC48606.2020.9185719.

2021¶

[283] J. Faure and A. Engelbrecht, “A Convolutional Neural Network for Dental Panoramic Radiograph Classification,” in Proceedings of the 5^th international conference on intelligent systems, metaheuristics & swarm intelligence, 2021, doi: https://doi.org/10.1145/3461598.3461607.

[284] T. Crane, A. Engelbrecht, and B. Ombuki-Berman, “Two Modified NichePSO Algorithms for Multimodal Optimization,” in Proceedings of the international conference on swarm intelligence, vol. 12689, Y. Tan and Y. Shi, Eds. 2021, pp. 232–243.

[285] J. van Zyl and A. Engelbrecht, “Polynomial Approximation Using Set-Based Particle Swarm Optimization,” in Proceedings of the international conference on swarm intelligence, vol. 12689, Y. Tan and Y. Shi, Eds. 2021, pp. 210–222.

[286] T. Carolus and A. Engelbrecht, “Multi-Guide Particle Swarm Optimisation Control Parameter Importance in High Dimensional Spaces,” in Proceedings of the international conference on swarm intelligence, vol. 12689, Y. Tan and Y. Shi, Eds. 2021, pp. 185–198.

[287] K. Harrison, B. Ombuki-Berman, and A. Engelbrecht, “Visualizing and Characterizing the Parameter Configuration Landscape of Particle Swarm Optimization using Physical Landform Classification,” in Proceedings of the ieee congress on evolutionary computation, 2021, doi: https://doi.org/10.1109/CEC45853.2021.9504760.

[288] C. Dennis, B. Ombuki-Berman, and A. Engelbrecht, “Predicting Particle Swarm Optimization Control Parameters from Fitness Landscape Characteristics,” in Proceedings of the ieee congress on evolutionary computation, 2021, doi: https://doi.org/10.1109/CEC45853.2021.9505006.

[289] A. Madani, B. Ombuki-Berman, and A. Engelbrecht, “Decision Space Scalability Analysis of Multi-Objective Particle Swarm Optimization Algorithms,” in Proceedings of the ieee congress on evolutionary computation, 2021, doi: https://doi.org/10.1109/CEC45853.2021.9504846.

[290] J. Faure and A. Engelbrecht, “Impacted Tooth Detection in Panoramic Radiographs,” in Proceedings of the 16^th international work-conference on artificial neural networks, vol. 12861, I. Rojas, G. Joya, and A. Català, Eds. 2021, pp. 525–536.

[291] S. van Deventer and A. Engelbrecht, “Learning to Trade from Zero-Knowledge using Particle Swarm Optimization,” in Proceedings of the 16^th international work-conference on artificial neural networks, vol. 12862, I. Rojas, G. Joya, and A. Català, Eds. 2021, pp. 183–195.

[292] K. Erwin and A. Engelbrecht, “Set-based Particle Swarm Optimization for Portfolio Optimization with Adaptive Coordinate Descent Weight Optimization,” in Proceedings of the ieee swarm intelligence symposium, 2021, doi: https://doi.org/10.1109/SSCI50451.2021.9659541.

[293] K. Erwin and A. Engelbrecht, “A Tuning Free Approach to Multi-guide Particle Swarm Optimization,” in Proceedings of the ieee swarm intelligence symposium, 2021, doi: https://doi.org/10.1109/SSCI50451.2021.9660050.

2022¶

[294] W. Steyn and A. Engelbrecht, “Stability-guided multi-guide particle swarm optimization,” in International conference on intelligent systems, meta-heuristics & swarm intelligence, 2022, doi: https://doi.org/10.1145/3533050.3533059.

[295] L. Brown and A. Engelbrecht, “Set-based particle swarm optimization for data clustering,” in International conference on intelligent systems, meta-heuristics & swarm intelligence, 2022, doi: https://doi.org/10.1145/3533050.3533057.

[296] D. Edeling and A. Engelbrecht, “Static polynomial approximation using set-based particle swarm optimisation,” in International conference on intelligent systems, meta-heuristics & swarm intelligence, 2022, pp. 67–72, doi: https://doi.org/10.1145/3533050.3533061.

[297] G. Cenikj, R. Lang, A. Engelbrecht, C. Doerr, and P. Korosec, “SELECTOR: Selecting a representative benchmark suite for reproducible statistical comparison,” in Genetic and evolutionary computation conference, 2022, pp. 620–629, doi: https://doi.org/10.1145/3512290.3528809.

[298] K. Erwin and A. Engelbrecht, “Improved hamming diversity measure for set-based optimization algorithms,” in International conference on swarm intelligence, vol. 13344, Y. Tan, Y. Shi, and B. Niu, Eds. 2022, pp. 39–47.

[299] J.-P. van Zyl and A. Engelbrecht, “Rule induction using set-based particle swarm optimization,” in IEEE congress on evolutionary computation, 2022, doi: https://doi.org/10.1109/CEC55065.2022.9870360.

[300] P. Joćko, B. Ombuki-Berman, and A. Engelbrecht, “Dynamic multi-objective optimisation using multi-guide particle swarm optimisation,” in IEEE congress on evolutionary computation, 2022, doi: https://doi.org/10.1109/CEC55065.2022.9870299.

[301] A. McNulty, B. Ombuki-Berman, and A. Engelbrecht, “Decomposition and merging co-operative particle swarm optimization with random grouping,” in Internatinal conference on swarm intelligence, vol. 13491, 2022, pp. 117–129.

[302] W. Steyn and A. Engelbrecht, “Dynamic spatial guided multi-guide particle swarm optimization for many-objective optimization,” in Internatinal conference on swarm intelligence, vol. 13491, 2022, pp. 130–141.

[303] A. Engelbrecht, “Stability-guided particle swarm optimization,” in Internatinal conference on swarm intelligence (ants), vol. 13491, 2022, pp. 360–369.

[304] M. Clark, B. Ombuki-Berman, N. Aksamit, and A. Engelbrecht, “Cooperative particle swarm optimization decomposition methods for large-scale optimization,” in IEEE symposium on cooperative metaheuristics, 2022.

[305] L. McDevitt, B. Ombuki-Berman, and A. Engelbrecht, “A particle swarm optimization decomposition strategy for large scale global optimization,” in IEEE symposium on cooperative metaheuristics, 2022.

2023¶

[306] A. Bosman, A. Engelbrecht, and M. Helbig, “Empirical loss surface analysis of neural network activation functions,” in Genetic and evolutionary computation conference, 2023.

[307] R. de Wet and A. Engelbrecht, “Set-based particle swarm optimization for data clustering: Comparison and analysis of control parameters,” in International conference ons intelligent systems, metaheuristics & swarm intelligence, 2024.

[308] K. Erwin and A. Engelbrecht, “Meta–heuristics for portfolio optimization: Part i — review of meta-heuristics,” in Fourteenth international conference on swarm intelligence, vol. 13969, 2023, pp. 441–452.

[309] K. Erwin and A. Engelbrecht, “Meta-heuristics for portfolio optimization: Part ii - empirical analysis,” in Fourteenth international conference on swarm intelligence, vol. 13969, 2023, pp. 453–464.

[310] L. Hayward and A. Engelbrecht, “How to tell a fish from a bee: Constructing meta-heuristic search behaviour characteristics,” in Genetic and evolutionary computation conference, 2023.

[311] R. Lang and A. Engelbrecht, “Performance analysis of hybrid sampling and evolutionary algorithms,” in Genetic and evolutionary computation conference, 2023.

[312] A. Nikolikj et al., “Assessing the generalizability of a performance predictive model,” in Genetic and evolutionary computation conference, 2023.

[313] D. von Eschwege and A. Engelbrecht, “A cautionary note on poli’s stability condition for particle swarm optimization,” in IEEE swarm intelligence symposium, 2023.

2024¶

[314] Z. McGovarin and A. E. and BM Ombuki-Berman, “Stochastic grouping and subspace-based initialization in decomposition and merging cooperative particle swarm optimization for large-scale optimization problems,” in 37^th canadian artificial intelligence conference, 2024.

[315] M. Spangenberg and A. Engelbrecht, “Set-based particle swarm optimization for the multi-objective multi-dimensional knapsack problem,” in International conference on swarm intelligence, vol. 14788, 2024, pp. 3–19.