A method of machine learning based on the simulation of Darwinian evolution and natural selection. It involves using a fitness function to rate a group of individual identities, each having a defining "genetic" formula or code. The fitness function is applied repeatedly to the individuals who may survive to the next generation, be replaced by a new individual, or cross-breed and mutate their genetic codes with other individuals. The method for determining survival of individual genes varies between implementations.

A type of EVOLUTIONARY COMPUTATION devised by John Holland [HOLLAND92]. A model of machine learning that uses a genetic/evolutionary metaphor. Implementations typically use fixed-length character strings to represent their genetic information, together with a POPULATION of INDIVIDUAL which undergo CROSSOVER and MUTATION in order to find interesting regions of the SEARCH SPACE. See Q1.1 for more information.

based on genetics and evolution;

Method for library design by evaluating the fit of a parent library to some desired property (e.g. the level of activity in a biological assay or the computationally determined diversity of the compound set) as measured by a fitness function. The design of more optimal daughter libraries is then carried out by a heuristic process with similarities to genetic selection in that it employs replication, mutation, deletions, etc. over a number of generations.

Any algorithm which seeks to solve a problem by considering numerous possibilities at once, ranking them according to some standard of fitness, and then combining ("breeding") the fittest in some way. In other words, any algorithm which imitates natural selection. [AS

Learning principle which found optimal solution over a large set of feasible solutions by generating, crossing and deleting them through a paths which recalls biological populations evolution

a class of adaptive stochastic optimization algorithms involving search and optimization

a computational model that imitates the process of Darwinian evolution

a computer method for finding solutions to complex problems based on an electronic version of natural selection and genetics

a form of computer based search often used in parameter optimisation problems

a general-purpose method for searching through a large number of objects to find one that optimizes a quantity of interest, using methods derived from Darwinian evolution

a heuristic search algorithm for the solution of optimization problems in which, starting from a random

a heuristic that mimics the evolution of natural species in searching for the optimal solution to a problem

a learning system for numerical optimization that works with populations of individuals, usually represented as binary vectors

a method of performing search and optimization based on the concepts of Darwinian evolution

a model of machine data sorting which derives its behavior by analogy to certain mechanisms of evolution in nature

an optimization technique that uses randomization instead of a deterministic search strategy

a opimization method that imitates the process of natural evolution

a process that seeks to create a computational model that uses bits of ideas from sexual reproduction and natural selection

a search procedure based on the mechanics

a search technique that uses concepts from reproduction and natural selection to produce better solutions (children) from previous solutions (parents)

a system for optimization of non-linear problems

A method of SIMULATING the action of EVOLUTION within a computer. A population of fixed-length STRINGS is evolved with a GA by employing CROSSOVER and MUTATION operators along with a FITNESS FUNCTION that determines how likely individuals are to reproduce. Gas perform a type of SEARCH in a FITNESS LANDSCAPE.

The use of evolutionary techniques to diversify, combine and select options in order to improve performance, following the methods of natural selection by coding options as genes.

A population containing a number of trial solutions each of which is evaluated (to yield a fitness) and a new generation is created from the better of them. The process is continued through a number of generations with the aim that the population should evolve to contain an acceptable solution. GAs are characterised by representing the solution as an (often fixed length) string of digital symbols, selecting parents from the current population in proportion to their fitness (or some approximation of this) and the use of crossover as the dominate means of creating new members of the population. The initial population may be created at random or from some known starting point.

An evolutionary algorithm that employs data crossover and mutation to derive ever-more-accurate results. This how trimMail Inbox slashes your spam at the same time it minimizes your false positives.

An algorithm for combinatorial optimization problems inspired by genetics. Possible solutions are represented by individuals in one or more populations and operations on possible solutions (individuals) corresponding to genetic processes (e.g., mutation, recombination, selection, migration).

A search algorithm which locates optimal binary strings by processing an initially random population of strings using artificial mutation, crossover and selection operators, in an analogy with the process of natural selection.

An algorithm that uses fuzzy logic and can refine itself based on its ability to select proper answers. Often, a human must tell the algorithm what it...

(GA) An AI technique that solves problems by simulating the "survival of the fittest" among possible solutions.

A genetic algorithm (or short GA) is a search technique used in computing to find true or approximate solutions to optimization and search problems. Genetic algorithms are categorized as global search heuristics. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).