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Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution

References [1] Gong, Y.J., Chen, W.N., Zhan, Z.H., Zhang, J., Li, Y., Zhang, Q. and Li, J.J., 2015, Distributed evolutionary algorithms and their models: A survey of the state-of-the-art, Applied Soft Computing, 34, pp. 286-300. DOI: 10.1016/j.asoc.2015.04.061 [2] Zelinka, I., 2015, A survey on evolutionary algorithms dynamics and its complexity–Mutual relations, past, present and future, Swarm and Evolutionary Computation, 25, pp. 2-14. DOI: 10.1016/j.swevo.2015.06.002 [3] Price, K., Storn, R.M. and Lampinen, J.A., 2006, Differential evolution

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Effect of Strategy Adaptation on Differential Evolution in Presence and Absence of Parameter Adaptation: An Investigation

References [1] A. E. Eiben, R. Hinterding, Z. Michalewicz, Parameter control in evolutionary algorithms, IEEE Transactions on Evolutionary Computation, 3 (2), 124–141, 1999. [2] G. Beni, J. Wang, Swarm Intelligence in Cellular Robotic Systems, in: Proceedings of the NATO Advanced Workshop on Robots and Biological Systems. Tuscany, Italy, 1989. [3] P.J. Angeline, Adaptive and self-adaptive evolutionary computation, in: M. Palaniswami, Y. Attikiouzel, R.J. Marks, D.B. Fogel, T. Fukuda (Eds.), Computational Intelligence: A Dynamic System

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Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

causal loop diagram-like structures for system dynamics modeling using echo state networks, Syst. Dynam. Rev. - In Press, 2017. [14] D. E. Goldberg, Genetic algorithms. Pearson Education India, 2006. [15] R. Storn and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces, J. Global. Optim., vol. 11, no. 4, pp. 341–359, 1997. [16] J. Kennedy, Particle swarm optimization, in Encyclopedia of machine learning, pp. 760–766, Springer, 2011. [17] Z. Wang, J. Zhang, J. Ren, and M. N. Aslam, A

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Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

References [1] A. Fernandez, S. Garcia, J. Luengo, E. Bernado- Mansilla, F. Herrera, Genetics-Based Machine Learning for Rule Induction: State of the Art, Taxonomy, and Comparative Study, Evolutionary Computation, IEEE Transactions on (Volume:14, Issue: 6), June 21, 2010, pp. 913 - 941. [2] L. B. Booker, D. E. Goldberg, and J. H. Holland, Classifier systems and genetic algorithms, Artif. Intell., vol. 40, no. 1-3, Sep. 1989, pp. 235-282. [3] Bodenhofer U., Herrera F. Ten Lectures on Genetic Fuzzy Systems

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Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning

logistics. IEEE Access, 5:26892 – 26900, 10 2017. [7] Sumana Biswas, Sreenatha G. Anavatti, and Matthew A. Garratt. Obstacle avoidance for multi-agent path planning based on vectorized particle swarm optimization. In George Leu, Hemant Kumar Singh, and Saber Elsayed, editors, Intelligent and Evolutionary Systems, pages 61–74, Cham, 2017. Springer International Publishing. [8] E. A. S. Carballo, L. Morales, and F. Trujillo-Romero. Path planning for a mobile robot using genetic algorithm and artificial bee colony. In 2017 International Conference on Mechatronics

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MIDACO Parallelization Scalability on 200 MINLP Benchmarks

References [1] Babu B., Angira A., A differential evolution approach for global optimisation of minlp problems, In: Proceedings of the Fourth Asia Pacific Conference on Simulated Evolution and Learning (SEAL 2002), Singapore, 2002, pp. 880–884. [2] Cardoso M.F., Salcedo R.L., Azevedo S.F., Barbosa D., A simulated annealing approach to the solution of MINLP problems, Computers Chem. Engng. 12(21), 1997, pp. 1349–1364. [3] Costa L., Oliveira P., Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems

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Optimization of Traveling Salesman Problem Using Affinity Propagation Clustering and Genetic Algorithm

References [1] Liao Y.-F., Yau D.-H., Chen C.-L., Evolutionary algorithm to traveling salesman problems, Computers & Mathematics with Applications, Vol. 64, Issue 5, pages 788-797, 2012. [2] Leung, K.S., Jin H.D., & Xu, Z.B., An expanding self-organizing neural network for the traveling salesman problem, Neurocomputing, 62, 267-292. Masutti, T.A.S., & de Castro, L.N. (2004). [3] Masutti, T.A.S., & de Castro, L.N., A selforganizing neural network using ideas from the immune system to solve the traveling salesman

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Adapting Differential Evolution Algorithms For Continuous Optimization Via Greedy Adjustment Of Control Parameters

References [1] N. Xiong, D. Molina, M. Leon, and F. Herrera, A walk into metaheuristics for engineering optimization: Principles, methods, and recent trends, International Journal of Computational Intelligence Systems, vol. 8, no. 4, pp. 606-636, 2015. [2] N. Hansen and A. Ostermeier, Completely derandomized self-adaptation in evolution strategies, Evolutionary Computation, vol. 9, no. 2, pp. 159-195, 2001. [3] F. Herrera and M. Lozano, Two-loop real-coded genetic algorithms with adaptive control of mutation

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Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition

-II, IEEE Transactions on Evolutionary Computation 6 (2), 2002, pp. 182-197. [7] R. Wang, Preference-Inspired Co-evolutionary Algorithms, A thesis submitted in partial fulfillment for the degree of the Doctor of Philosophy, University of Sheffield, 2013, p. 231. [8] E. Zitzler, M. Laumanns, L. Thiele, SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization, Evolutionary Methods for Design Optimisation and Control with Application to Industrial Problems EUROGEN 2001 3242 (103), 2002, pp. 95

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Parallel Pbil Applied to Power System Controller Design

, Hyper-Learning for Population-Based Incremental Learning in Dynamic Environment, In: IEEE Congress on Evolutionary Computation, 2009. [24] M. Ventresca, H. R. Tizhoosh, A diversity Maintaining Population Based Incremental Learning Algorithm, Information Sciences, 178, 2008, pp. 4038-4056 [25] S. Y. Yang, S.L. Ho, G.Z. Ni, J.M. Machado and K.F.Wong, A new Implementation of Population- Based Incremental Learning Method for Optimizations in Electromagnetics, IEEE Trans. On Magnetics 43 (4), 2007, pp. 1601-1604. [26] S. Yang

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