Geodesic
Cooperation of bio-inspired and evolutionary algorithms for neural network design
Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 11 (2018) no. 2, pp. 148-158.

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A meta-heuristic called Co-Operation of Biology-Related Algorithms (COBRA) with a fuzzy controller, as well as a new algorithm based on the cooperation of Differential Evolution and Particle Swarm Optimization (DE+PSO) and developed for solving real-valued optimization problems, were applied to the design of artificial neural networks. The usefulness and workability of both meta-heuristic approaches were demonstrated on various benchmarks. The neural network's weight coefficients represented as a string of real-valued variables are adjusted with the fuzzy controlled COBRA or with DE+PSO. Two classification problems (image and speech recognition problems) were solved with these approaches. Experiments showed that both cooperative optimization techniques demonstrate high performance and reliability in spite of the complexity of the solved optimization problems. The workability and usefulness of the proposed meta-heuristic optimization algorithms are confirmed.
Keywords: co-operation, bio-inspired algorithms, differential evolution, neural networks
Mots-clés : classification. 
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Shakhnaz A. Akhmedova; Vladimir V. Stanovov; Eugene S. Semenkin. Cooperation of bio-inspired and evolutionary algorithms for neural network design. Žurnal Sibirskogo federalʹnogo universiteta. Matematika i fizika, Tome 11 (2018) no. 2, pp. 148-158. http://geodesic.mathdoc.fr/item/JSFU_2018_11_2_a2/

[1] M.A.Potter, K.A.DeJong, “A Cooperative Coevolutionary Approach to Function Optimization”, Parallel Problem Solving from Nature – PPSN III, 1994, 249–257 | DOI

[2] Sh.Akhmedova, E.Semenkin, “Co-Operation of Biology Related Algorithms”, Proceedings of the IEEE Congress on Evolutionary Computation (2013), 2207–2214

[3] J.Kennedy, R.Eberhart, “Particle Swarm Optimization”, Proceedings of the IV International Conference on Neural Networks, 1995, 1942–1948 | DOI

[4] R.Storn, K.Price, “Differential evolution — a simple and efficient heuristic for global optimization over continuous spaces”, Journal of Global Optimization, 11:4 (1997), 341–359 | DOI | MR | Zbl

[5] Ch.Yang, X.Tu, J.Chen, “Algorithm of Marriage in Honey Bees Optimization Based on the Wolf Pack Search”, Proceedings of the International Conference on Intelligent Pervasive Computing, 2007, 462–467

[6] X.S.Yang, “Firefly algorithms for multimodal optimization”, Proceedings of the 5th Symposium on Stochastic Algorithms, Foundations and Applications, 2009, 169–178 | DOI | MR | Zbl

[7] X.S.Yang, S.Deb, “Cuckoo Search via Levy flights”, Proceedings of the World Congress on Nature and Biologically Inspired Computing, 2009, 210–214 | DOI

[8] X.S.Yang, “A new metaheuristic bat-inspired algorithm”, Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, 284, 2010, 65–74 | Zbl

[9] F.C.Bastos, N.F.Lima, “Fish School Search: an overview”, Nature-Inspired Algorithms for Optimization, Studies in Computational Intelligence, 193, 2009, 261-277

[10] C.-C.Lee, “Fuzzy logic in control systems: fuzzy logic controller – parts 1 and 2”, Transactions on Systems, Man, and Cybernetics, 20:2 (1990), 404–435 | DOI | MR

[11] Sh.Akhmedova, E.Semenkin, “Co-Operation of Biology Related Algorithms Meta-Heuristic in ANN-Based Classifiers Design”, Proceedings of the World Congress on Computational Intelligence, 2014, 867–873

[12] J.J.Liang, B.Y.Qu, P.N.Suganthan, A.G.Hernandez-Diaz, Problem Definitions and Evaluation Criteria for the CEC 2013 Special Session on Real-Parameter Optimization, Technical Report, Zhengzhou University, Nanyang Technological University, Singapore, 2012, 867–873 | Zbl

[13] A.Frank, A.Asuncion, UCI Machine Learning Repository, (accessed 2010) http://archive.ics.uci.edu/ml

[14] E.Semenkin, V.Stanovov, “Fuzzy Rule Bases Automated Design with Self-Configuring Evolutionary Algorithm”, Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics, 2014, 318–323