evolutionary computing - significado y definición. Qué es evolutionary computing
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Qué (quién) es evolutionary computing - definición

SUBFIELD OF ARTIFICIAL INTELLIGENCE
Evolutionary Computation; Evolutionary computing; Virtual creature; Virtual animal; Evolved virtual creatures; Datanetics; Evolution simulation; Computer simulations of evolution; History of evolutionary computation

evolutionary computation         
Computer-based problem solving systems that use computational models of evolutionary processes as the key elements in design and implementation. A number of evolutionary computational models have been proposed, including evolutionary algorithms, {genetic algorithms}, the evolution strategy, {evolutionary programming}, and artificial life. {The Hitchhiker's Guide to Evolutionary Computation (http://cis.ohio-state.edu/hypertext/faq/bngusenet/comp/ai/genetic/top.html)}. {Bibliography (http://liinwww.ira.uka.de/bibliography/Ai/EC-ref.html)}. Usenet newsgroup: news:comp.ai.genetic. (1995-03-02)
Evolutionary computation         
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. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
Convergence (evolutionary computing)         
TERM IN EVOLUTIONARY COMPUTING
Convergence in evolutionary computing
Convergence within the field of computer science, is a phenomenon in evolutionary computation. It causes evolution to halt because precisely every individual in the population is identical.

Wikipedia

Evolutionary computation

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. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.

In evolutionary computation, an initial set of candidate solutions is generated and iteratively updated. Each new generation is produced by stochastically removing less desired solutions, and introducing small random changes. In biological terminology, a population of solutions is subjected to natural selection (or artificial selection) and mutation. As a result, the population will gradually evolve to increase in fitness, in this case the chosen fitness function of the algorithm.

Evolutionary computation techniques can produce highly optimized solutions in a wide range of problem settings, making them popular in computer science. Many variants and extensions exist, suited to more specific families of problems and data structures. Evolutionary computation is also sometimes used in evolutionary biology as an in silico experimental procedure to study common aspects of general evolutionary processes.