By Jürgen Branke
Evolutionary Algorithms (EAs) have grown right into a mature box of study in optimization, and feature confirmed to be potent and powerful challenge solvers for a large diversity of static real-world optimization difficulties. but, because they're in keeping with the rules of normal evolution, and because traditional evolution is a dynamic strategy in a altering setting, EAs also are compatible to dynamic optimization difficulties. Evolutionary Optimization in Dynamic Environments is the 1st complete paintings at the program of EAs to dynamic optimization difficulties. It offers an in depth survey on examine in the world and indicates how EAs could be effectively used to
- consistently and successfully adapt an answer to a altering surroundings,
- discover a stable trade-off among answer caliber and edition fee,
- locate powerful options whose caliber is insensitive to alterations within the setting,
- locate versatile suggestions which aren't purely stable yet that may be simply tailored while invaluable.
All 4 features are handled during this publication, delivering a holistic view at the demanding situations and possibilities whilst using EAs to dynamic optimization difficulties. the excellent and updated insurance of the topic, including info of contemporary unique examine, makes Evolutionary Optimization in Dynamic Environments a useful source for researchers and pros who're facing dynamic and stochastic optimization difficulties, and who're in utilizing neighborhood seek heuristics, corresponding to evolutionary algorithms.
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