evolutionary algorithm

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Related to evolutionary algorithm: Genetic algorithm

evolutionary algorithm

(EA) An algorithm which incorporates aspects of natural selection or survival of the fittest. An evolutionary algorithm maintains a population of structures (usually randomly generated initially), that evolves according to rules of selection, recombination, mutation and survival, referred to as genetic operators. A shared "environment" determines the fitness or performance of each individual in the population. The fittest individuals are more likely to be selected for reproduction (retention or duplication), while recombination and mutation modify those individuals, yielding potentially superior ones.

EAs are one kind of evolutionary computation and differ from genetic algorithms. A GA generates each individual from some encoded form known as a "chromosome" and it is these which are combined or mutated to breed new individuals.

EAs are useful for optimisation when other techniques such as gradient descent or direct, analytical discovery are not possible. Combinatoric and real-valued function optimisation in which the optimisation surface or fitness landscape is "rugged", possessing many locally optimal solutions, are well suited for evolutionary algorithms.
This article is provided by FOLDOC - Free Online Dictionary of Computing (foldoc.org)
References in periodicals archive ?
Section 5 introduces the constrained multi-objective evolutionary algorithm. The simulation results and an example analysis is shown in section 6.
Multi-objective Evolutionary Algorithms (MOEA) follow the same intent, however, by leveraging computing power so that hundreds of solutions may be assessed.
An evolutionary algorithm for solving equations systems inspired from political life -very close to what we call today globalization - named Imperialist Competitive Algorithm (ICA) [5] is proposed in [3].
Like other evolutionary algorithms, TLBO is also based on population method, which employs a population of solution to proceed for the search of the optimum solution.
Pan, "A radial space division based evolutionary algorithm for many-objective optimization," Applied Soft Computing, vol.
Lau, "Immunity-based hybrid evolutionary algorithm for multi-objective optimization in global container repositioning," Engineering Applications of Artificial Intelligence, vol.
Clustering large probabilistic graphs using multi-population evolutionary algorithm. Information Sciences, Elsevier Inc.
However, it is well known that multiobjective evolutionary algorithms can lose their effectiveness on problems with more than 3 objectives.
Zheng, "A grid-based evolutionary algorithm for many-objective optimization," IEEE Transactions on Evolutionary Computation, vol.
Multiobjective evolutionary algorithms (MOEAS) have been proposed to resolve multiobjective problems, for instance, the nondominated sorting genetic algorithm II (NSGA-II) [7] by Deb et al.

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