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Evolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming , but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve.
Evolutionary programming – Similar to genetic programming, but the structure of the program is fixed and its numerical parameters are allowed to evolve. Evolution strategy – Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms.
The resulting algorithm is therefore invariant with respect to monotonic transformations of the objective function. The simplest evolution strategy operates on a population of size two: the current point (parent) and the result of its mutation.
In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism.
Selection is the stage of a genetic algorithm or more general evolutionary algorithm in which individual genomes are chosen from a population for later breeding (e.g., using the crossover operator). Selection mechanisms are also used to choose candidate solutions (individuals) for the next generation.
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.
Provides evolutionary algorithms, genetic programming algorithms, and allows customized algorithms, along with specification of arbitrary constraints. Evolvable aspects include the neural model and allows for the evolution of morphology and sexual selection among others.