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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 population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population is the set of all proposed solutions of an EA considered in one iteration, which are also called individuals according to the biological role model.
Evolutionary algorithms (EAs) due to their population based approach, provide a natural advantage over classical optimization techniques. They maintain a population of possible solutions, which are processed every generation, and if the multiple solutions can be preserved over all these generations, then at termination of the algorithm we will ...
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.
A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm (GA) or more general evolutionary algorithm (EA). It may provide a sufficiently good solution to an optimization problem .
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 ...
ECJ is a freeware evolutionary computation research system written in Java. It is a framework that supports a variety of evolutionary computation techniques, such as genetic algorithms, genetic programming, evolution strategies, coevolution, particle swarm optimization, and differential evolution.
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.