Chromosome (genetic algorithm)

In genetic algorithms, a chromosome (also sometimes called a genotype) is a set of parameters which define a proposed solution to the problem that the genetic algorithm is trying to solve. ^[1]

7 relations: Crossover (genetic algorithm), Data structure, Genetic algorithm, Genetic operator, Mutation (genetic algorithm), String (computer science), Travelling salesman problem.

Crossover (genetic algorithm)

In genetic algorithms and evolutionary computation, crossover, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring.

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Data structure

In computer science, a data structure is a data organization and storage format that enables efficient access and modification.

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Genetic algorithm

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).

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Genetic operator

A genetic operator is an operator used in genetic algorithms to guide the algorithm towards a solution to a given problem.

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Mutation (genetic algorithm)

Mutation is a genetic operator used to maintain genetic diversity from one generation of a population of genetic algorithm chromosomes to the next.

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String (computer science)

In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable.

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The travelling salesman problem (TSP) asks the following question: "Given a list of cities and the distances between each pair of cities, what is the shortest possible route that visits each city and returns to the origin city?" It is an NP-hard problem in combinatorial optimization, important in operations research and theoretical computer science.

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Genome (genetic algorithm).

References

[1] https://en.wikipedia.org/wiki/Chromosome_(genetic_algorithm)

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