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When applying both population models to genetic algorithms, [5] [6] evolutionary strategy [20] [17] [21] and other EAs, [22] [23] the splitting of a total population into subpopulations usually reduces the risk of premature convergence and leads to better results overall more reliably and faster than would be expected with panmictic EAs. [2] [6]
Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve “difficult” problems, at least approximately, for which no exact or satisfactory solution methods are known.
The field of Evolutionary algorithms encompasses genetic algorithms (GAs), evolution strategy (ES), differential evolution (DE), particle swarm optimization (PSO), and other methods. Attempts have been made to solve multi-modal optimization in all these realms and most, if not all the various methods implement niching in some form or the other.
Evolutionary computation from computer science is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
Many EAs, such as the evolution strategy [10] [11] or the real-coded genetic algorithms, [12] [13] [8] work with real numbers instead of bit strings. This is due to the good experiences that have been made with this type of coding.
An evolutionary algorithm (EA) is a heuristic optimization algorithm using techniques inspired by mechanisms from organic evolution such as mutation, recombination, and natural selection to find an optimal configuration for a specific system within specific constraints.
Evolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization technique. [1] It uses the major genetic operators mutation , recombination and selection of parents .
A cellular evolutionary algorithm (cEA) usually evolves a structured bidimensional grid of individuals, although other topologies are also possible. In this grid, clusters of similar individuals are naturally created during evolution, promoting exploration in their boundaries, while exploitation is mainly performed by direct competition and ...