Genetic operator
A genetic operator is an
Mutation (or mutation-like) operators are said to be unary operators, as they only operate on one chromosome at a time. In contrast, crossover operators are said to be binary operators, as they operate on two chromosomes at a time, combining two existing chromosomes into one new chromosome.[4]
Operators
Genetic variation is a necessity for the process of evolution. Genetic operators used in genetic algorithms are analogous to those in the natural world: survival of the fittest, or selection; reproduction (crossover, also called recombination); and mutation.
Selection
Selection operators give preference to better solutions (chromosomes), allowing them to pass on their 'genes' to the next generation of the algorithm. The best solutions are determined using some form of
Crossover
Crossover is the process of taking more than one parent solutions (chromosomes) and producing a child solution from them. By recombining portions of good solutions, the genetic algorithm is more likely to create a better solution.[1] As with selection, there are a number of different methods for combining the parent solutions, including the edge recombination operator (ERO) and the 'cut and splice crossover' and 'uniform crossover' methods. The crossover method is often chosen to closely match the chromosome's representation of the solution; this may become particularly important when variables are grouped together as building blocks, which might be disrupted by a non-respectful crossover operator. Similarly, crossover methods may be particularly suited to certain problems; the ERO is generally considered a good option for solving the travelling salesman problem.[6]
Mutation
The mutation operator encourages genetic diversity amongst solutions and attempts to prevent the genetic algorithm converging to a
Combining operators
While each operator acts to improve the solutions produced by the genetic algorithm working individually, the operators must work in conjunction with each other for the algorithm to be successful in finding a good solution. Using the selection operator on its own will tend to fill the solution population with copies of the best solution from the population. If the selection and crossover operators are used without the mutation operator, the algorithm will tend to converge to a
References
- ^ a b c d e "Introduction to Genetic Algorithms". Archived from the original on 11 August 2015. Retrieved 20 August 2015.
- ISBN 0-262-11170-5.
- ^ "Genetic programming operators". Retrieved 20 August 2015.
- ^ "Genetic operators". Archived from the original on 30 December 2017. Retrieved 20 August 2015.
- ^ "Introduction to Genetic Algorithm". Retrieved 20 August 2015.
- ISBN 1558600663.)
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