In genetic algorithms, mutation is a genetic operator used to maintain genetic diversity from one generation of a population of chromosomes to the next. It is analogous to biologicalmutation. A genetic algorithm (GA) is a search technique used in computer science to find approximate solutions to optimization and search problems. ... A genetic operator is a process used in genetic algorithms to maintain genetic diversity. ... Genetic diversity is a characteristic of ecosystems and gene pools that describes an attribute which is commonly held to be advantageous for survival -- that there are many different versions of otherwise similar organisms. ... In genetic algorithms, a chromosome (also sometimes called a genome) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve. ... Biology is the branch of science dealing with the study of life. ... In biology, mutations are changes to the genetic material (usually DNA or RNA). ...
The classic example of a mutation operator involves a probability that an arbitrary bit in a genetic sequence will be changed from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence. This random variable tells whether or not a particular bit will be modified. A bit refers to a digit in the binary numeral system (base 2). ... In genetic algorithms, a chromosome (also sometimes called a genome) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve. ... A random variable is a mathematical function that maps statistical events to numbers. ...
The purpose of mutation in GAs is to allow the algorithm to avoid local minima by preventing the population of chromosomes from becoming too similar to each other, thus slowing or even stopping evolution. This reasoning also explains the fact that most GA systems avoid only taking the fittest of the population in generating the next but rather a random (or semi-random) selection with a weighting toward those that are fitter. A graph illustrating local min/max and global min/max points In mathematics, a point x* is a local maximum of a function f if there exists some ε > 0 such that f(x*) ≥ f(x) for all x with |x-x*| < ε. ... In optimisation techniques an objective measure is how good the solutions it finds are. ...
Geneticalgorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).
Geneticalgorithms are implemented as a computer simulation in which a population of abstract representations (called chromosomes or the genotype or the genome) of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem evolves toward better solutions.
As a general rule of thumb geneticalgorithms might be useful in problem domains that have a complex fitness landscape as recombination is designed to move the population away from local optima that a traditional hill climbing algorithm might get stuck in.
This remarkable ability of geneticalgorithms to focus their attention on the most promising parts of a solution space is a direct outcome of their ability to combine strings containing partial solutions.
The geneticalgorithm exploits the higher-payoff, or "target," regions of the solution space, because successive generations of reproduction and crossover produce increasing numbers of strings in those regions.
Implicit parallelism also helps geneticalgorithms to cope with nonlinear problems - those in which the fitness of a string containing two particular building blocks may be much greater (or much smaller) than the sum of the fitnesses attributable to each building block alone.
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