evolutionary programming

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evolutionary programming

[‚ev·ə¦lü·shə‚ner·ē ′prō‚gram·iŋ]
(computer science)
Computer programming with genetic algorithms. Also known as evolutionary computation; genetic programming.

evolutionary programming

(EP) A stochastic optimisation strategy originally conceived by Lawrence J. Fogel in 1960.

An initially random population of individuals (trial solutions) is created. Mutations are then applied to each individual to create new individuals. Mutations vary in the severity of their effect on the behaviour of the individual. The new individuals are then compared in a "tournament" to select which should survive to form the new population.

EP is similar to a genetic algorithm, but models only the behavioural linkage between parents and their offspring, rather than seeking to emulate specific genetic operators from nature such as the encoding of behaviour in a genome and recombination by genetic crossover.

EP is also similar to an evolution strategy (ES) although the two approaches developed independently. In EP, selection is by comparison with a randomly chosen set of other individuals whereas ES typically uses deterministic selection in which the worst individuals are purged from the population.
References in periodicals archive ?
Chattopadhyay, "Short-term hydrothermal scheduling through evolutionary programming technique", Electric Power Systems Research, vol.
In general the mutation in evolutionary programming is Gaussian mutation.
The evolutionary programming is mainly used on heavily constrained problems.
Proceedings of the 5th Conference on Evolutionary Programming (EP '96), L.
I said, above, that the best prima facie examples of transformational AI involve evolutionary programming.
Evolutionary programming is a probabilistic, global search technique that starts with a population of randomly generated candidate solutions and evolves towards better solutions over a number of generations or iterations.
Most well known examples of EAs are Genetic Algorithms (GAs), Evolution Strategies (ESs), Evolutionary Programming (EP), and Genetic Programming (GP) [12].
The newest volume in this series presents refereed papers in the following categories and their application in the engineering domain: Neural networks, complex networks, evolutionary programming, data mining, fuzzy logic, adaptive control, pattern recognition, and smart engineering system design.
Among these kind of algorithms are evolutionary programming (EP) [9] and genetic programming (GP) [5], which have both a common origin in the imitation of natural evolution.
Another important evolutionary algorithm, and predecessor of genetic programming, was evolutionary programming.
However, there are other species of evolutionary algorithms, such as evolution strategies and evolutionary programming which are also used quite effectively.

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