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.
References in periodicals archive ?
12] Cao J, Zhang T, Zeng Z, et al, "Multi-relay selection schemes based on evolutionary algorithm in cooperative relay networks," International Journal of Communication Systems, vol.
Clustering large probabilistic graphs using multi-population evolutionary algorithm.
EDA is an evolutionary algorithm that can change the number of channels in any direction while trying to optimize the fitness function.
Squillero, "New evolutionary algorithm inspired by the selfish gene theory," in Proceedings of the IEEE Conference on Evolutionary Computation (ICEC '98), pp.
Hypothesis: Adaptive setting of control parameters of evolutionary algorithm based on number of estimated constants can positively influence ANN synthesis in accordance to its efficiency and speed.
In this customized evolutionary algorithm, the procedures of polynomial mutation have been embedded into SBX crossover to reduce the probability of mutation and thus stabilize the optimization process.
Some of these are known evolutionary algorithms NSGA, SPEA, NSGA-II, SPEA II and PAES-II, among others.
In computer science, evolutionary algorithms represent a kind of heuristic methodology inspired by evolutionary biology.
Li, "MOEA/D: A multiobjective evolutionary algorithm based on decomposition," IEEE Trans.
Combining P system and differential evolution operator, literature [3] present a hybrid distributed evolutionary algorithm for continuous optimization problems.
7]), the following existing hybrid multi-objective evolutionary algorithms are examined: (1) the genetic algorithm running on the internet (GAIN), (2) the parallel single front genetic algorithm (PSFGA), (3) the divided range multi-objective genetic algorithm (DRMOGA), (4) the parallel strength Pareto multi-objective evolutionary algorithm (PSPMEA), (5) the parallel multi-objective evolutionary algorithm with a hyper-graph represented population structure (pMOHypEA), (6) the multi-objective cellular genetic algorithm MOCell, (7) the simple multi-objective genetic local search algorithm (S-MOGLS), (8) the memetic Pareto-archived evolution strategy (M-PAES), (9) the hybrid EA (10) and hybrid NSGA-II (Stanley, Mudge 1995; de Toro et al.
Recently, artificial bee colony algorithm [15], ABC for short, is proposed as a population-based heuristic evolutionary algorithm inspired by the intelligent foraging behaviour of the honeybee swarm.

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