By Grosan C. (Ed), Abraham A. (Ed)
Hybridization of evolutionary algorithms is getting renowned because of their functions in dealing with a number of genuine global difficulties regarding complexity, noisy surroundings, imprecision, uncertainty and vagueness. This edited quantity is focused to give the most recent cutting-edge methodologies in "Hybrid Evolutionary Algorithms'. This ebook offers with the theoretical and methodological points, in addition to a number of functions to many actual international difficulties from technology, expertise, enterprise or trade. This quantity contains of 14 chapters together with an introductory bankruptcy giving the basic definitions and a few vital examine demanding situations. Chapters have been chosen at the foundation of primary ideas/concepts instead of the thoroughness of recommendations deployed.
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Extra info for Hybrid Evolutionary Algorithms
This success is due, besides other things, to the fact that these algorithms do not require previous information regarding the problem to be optimized and offer a high degree of parallelism. However, some problems are computationally intensive regarding the evaluation of each solution, which makes the optimization by EA’s slow in some situations. This chapter proposes a novel EA for numerical optimization inspired by the multiple universes principle of quantum computing that presents faster convergence time for the benchmark problems.
1942–1948 28. Kennedy J (1997) The particle swarm: social adaptation of knowledge, In Proceedings of IEEE International Conference on Evolutionary Computation, Indianapolis, IN, 1997, pp. 303–308 29. Kim DH and Cho JH (2005) Robust tuning of PID controller using bacterial-foragingbased optimization, JACIII 9(6), 669–676 30. Kim JH, Myung H, and Jeon JY (1995) Hybrid evolutionary programming with fast convergence for constrained optimization problems, IEEE Conference on Intelligent Systems for the 21 Century, Vancouver, Canada, pp.
295–310 21. ) (2005) Recent Advances in Memetic Algorithms, Series: Studies in Fuzziness and Soft Computing, Vol. 166 22. Herrera F and Lozano M (1996) Adaptation of genetic algorithm parameters based on Fuzzy logic controllers. ), pp. 95–125 23. Holland JH (1975) Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor, MI 1 Hybrid Evolutionary Algorithms 15 24. Hopper E and Turton BCH (2001) An empirical investigation of metaheuristic and heuristic algorithms for a 2D packing problem, European Journal of Operational Research 128, pp.
Hybrid Evolutionary Algorithms by Grosan C. (Ed), Abraham A. (Ed)