Difference between revisions of "Evolutionary computation"

Latest revision as of 10:41, 24 August 2016

In computer science, evolutionary computation is a subfield of artificial intelligence (more particularly soft computing) that can be defined by the type of algorithms it is concerned with.

Description

These algorithms, called evolutionary algorithms, are based on adopting Darwinian principles, hence the name. Technically they belong to the family of trial and error problem solvers and can be considered global optimization methods with a metaheuristic or stochastic optimization character, distinguished by the use of a population of candidate solutions (rather than just iterating over one point in the search space).

The application of recombination and evolutionary strategies makes them less prone to get stuck in local optima than alternative methods.

Evolutionary computation uses iterative progress, such as growth or development in a population. This population is then selected in a guided random search using parallel processing to achieve the desired end. Such processes are often inspired by biological mechanisms of evolution.

As evolution can produce highly optimised processes and networks, it has many applications in computer science.

See also

• Autoconstructive
• Estimation of distribution algorithm
• Evolutionary robotics
• Evolved antenna
• Fitness approximation
• Grammatical evolution
• Human-based evolutionary computation
• Inferential programming
• Interactive evolutionary computation
• Mutation testing
• No free lunch in search and optimization
• Universal Darwinism

External links

• Evolutionary computation @ Wikipedia