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In computer science and operations research, a genetic algorithm ( GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired ...
Genetic representation. In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between ...
In genetic algorithms (GA), or more general, evolutionary algorithms (EA), a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called individuals according to the biological model, is known as the ...
In artificial intelligence, genetic programming ( GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs. The operations are: selection of the fittest programs for reproduction ...
Different algorithms in evolutionary computation may use different data structures to store genetic information, and each genetic representation can be recombined with different crossover operators. Typical data structures that can be recombined with crossover are bit arrays , vectors of real numbers, or trees .
Gene expression programming. In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism.
A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem. There are three main types of operators ( mutation, crossover and selection ), which must work in conjunction with one another in order for the algorithm to be successful. Genetic operators are used to create and maintain ...
Filtering and signal processing [10] [11] Finding hardware bugs. [12] [13] Game theory equilibrium resolution. Genetic Algorithm for Rule Set Production. Scheduling applications, including job-shop scheduling and scheduling in printed circuit board assembly. [14] The objective being to schedule jobs in a sequence-dependent or non-sequence ...