### Abstract

One-point, two-point and k-point crossover can be viewed as special cases of uniform crossover, where genetic material is chosen each locus of either parent with equal probability. This paper generalizes uniform crossover to `non-uniform crossover' using `mask' vectors whose elements are real numbers qq[0,1], representing problem-specific knowledge that improves performance by biasing the selection of alleles from either parent. This knowledge based non-uniform crossover (KNUX) is applied to two NP optimization problems: graph partitioning and soft-decision decoding of linear block codes. Simulation results show orders of magnitude improvement of this operator over two-point and uniform crossover. An appropriate schema theorem is also developed.

Original language | English (US) |
---|---|

Title of host publication | IEEE Conference on Evolutionary Computation - Proceedings |

Publisher | IEEE Computer Society |

Pages | 22-27 |

Number of pages | 6 |

Volume | 1 |

State | Published - 1994 |

Event | Proceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994 |

### Other

Other | Proceedings of the 1st IEEE Conference on Evolutionary Computation. Part 1 (of 2) |
---|---|

City | Orlando, FL, USA |

Period | 6/27/94 → 6/29/94 |

### ASJC Scopus subject areas

- Engineering(all)

## Fingerprint Dive into the research topics of 'Knowledge-based nonuniform crossover'. Together they form a unique fingerprint.

## Cite this

*IEEE Conference on Evolutionary Computation - Proceedings*(Vol. 1, pp. 22-27). IEEE Computer Society.