Crossover operators that improve offspring fitness

Chilukuri K. Mohan

doi:10.1109/CEC.1999.782667

Crossover operators that improve offspring fitness

Chilukuri K. Mohan

Research output: Contribution to conference › Paper › peer-review

1 Scopus citations

Abstract

Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.

Original language	English (US)
Pages	1542-1549
Number of pages	8
DOIs	https://doi.org/10.1109/CEC.1999.782667
State	Published - 1999
Externally published	Yes
Event	1999 Congress on Evolutionary Computation, CEC 1999 - Washington, DC, United States Duration: Jul 6 1999 → Jul 9 1999

Other

Other	1999 Congress on Evolutionary Computation, CEC 1999
Country/Territory	United States
City	Washington, DC
Period	7/6/99 → 7/9/99

ASJC Scopus subject areas

Computational Mathematics

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10.1109/CEC.1999.782667

Cite this

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abstract = "Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.",

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AB - Fine-honing the crossover operator to produce higher fitness children is shown to result in improved genetic search. To illustrate this, two new general-purpose crossover operators are described. These operators require more computation time than traditional crossover operators, but the number of fitness evaluations and the overall amount of time spent by the genetic algorithm (to obtain solutions of desired near-optimal quality) is reduced significantly.

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T2 - 1999 Congress on Evolutionary Computation, CEC 1999

Y2 - 6 July 1999 through 9 July 1999

ER -

Crossover operators that improve offspring fitness

Abstract

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ASJC Scopus subject areas

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