By Jürgen Branke
Evolutionary Algorithms (EAs) have grown right into a mature box of learn in optimization, and feature confirmed to be powerful and strong challenge solvers for a vast variety of static real-world optimization difficulties. but, due to the fact that they're in accordance with the foundations of traditional evolution, and because normal evolution is a dynamic approach in a altering surroundings, EAs also are like minded to dynamic optimization difficulties. Evolutionary Optimization in Dynamic Environments is the 1st finished paintings at the software of EAs to dynamic optimization difficulties. It offers an intensive survey on learn in the realm and indicates how EAs may be effectively used to
- always and successfully adapt an answer to a altering setting,
- discover a stable trade-off among resolution caliber and variation fee,
- locate powerful suggestions whose caliber is insensitive to alterations within the surroundings,
- locate versatile suggestions which aren't simply reliable yet that may be simply tailored whilst priceless.
All 4 facets are taken care of during this e-book, supplying a holistic view at the demanding situations and possibilities whilst utilising EAs to dynamic optimization difficulties. the great and updated assurance of the topic, including information of contemporary unique study, 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 making use of neighborhood seek heuristics, comparable to evolutionary algorithms.
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