By Kenneth De Jong (auth.), Fernando G. Lobo, Cláudio F. Lima, Zbigniew Michalewicz (eds.)
One of the most problems of employing an evolutionary set of rules (or, in actual fact, any heuristic strategy) to a given challenge is to settle on a suitable set of parameter values. commonly those are special sooner than the set of rules is administered and comprise inhabitants dimension, choice price, operator percentages, let alone the illustration and the operators themselves. This booklet supplies the reader a great viewpoint at the assorted ways which have been proposed to automate keep an eye on of those parameters in addition to knowing their interactions. The publication covers a huge quarter of evolutionary computation, together with genetic algorithms, evolution options, genetic programming, estimation of distribution algorithms, and likewise discusses the problems of particular parameters utilized in parallel implementations, multi-objective evolutionary algorithms, and sensible attention for real-world functions. it's a urged learn for researchers and practitioners of evolutionary computation and heuristic methods.
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Extra info for Parameter Setting in Evolutionary Algorithms
The complete Covariance Matrix Adaptation (CMA-ES) algorithm was ﬁnally detailed (and its parameters carefully tuned) in  and an improvement for the update of the covariance matrix was proposed in . The basic idea in CMA-ES is to use the path followed by the algorithm to deterministically update the diﬀerent mutation parameters, and a simpliﬁed view is given by the following: suppose that the algorithm has made a series of steps in colinear directions; then the step-size should be increased, to allow larger steps and increase speed.
Indeed, although the selection acts based on the ﬁtness, the underlying idea beneath Self-Adaptive ES (SA-ES) is that if two individuals starts with the same ﬁtness, the oﬀspring of one that has “better” mutation parameters will reach regions of higher ﬁtness faster than the oﬀspring of the other: selection will hence keep the ones with the good mutation parameters. E. Eiben, Z. Michalewicz, M. E. Smith free” by the evolution itself. And indeed, SA-ES have long been the state-ofthe-art in parametric optimization .
Schwefel, editors, Proceedings of the 6th Conference on Parallel Problem Solving from Nature, number 1917 in Lecture Notes in Computer Science, pages 315–324. Springer, Berlin, Heidelberg, New York, 2000. 12. T. B. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computation. Institute of Physics Publishing, Bristol, and Oxford University Press, New York, 1997. 13. T. B¨ ack, M. Sch¨ utz, and S. Khuri. A comparative study of a penalty function, a repair heuristic and stochastic operators with set covering problem.