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Crossover Operators (crossover + operators)

Distribution by Scientific Domains
Information Science and Computing100%

Selected Abstracts

Hybrid crossover operators with multiple descendents for real-coded genetic algorithms: Combining neighborhood-based crossover operators

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 5 2009
Ana M. Sánchez
Most real-coded genetic algorithm research has focused on developing effective crossover operators, and as a result, many different types of crossover operators have been proposed. Some forms of crossover operators are more suitable to tackle certain problems than others, even at the different stages of the genetic process in the same problem. For this reason, techniques that combine multiple crossovers, called hybrid crossover operators, have been suggested as alternative schemes to the common practice of applying only one crossover model to all the elements in the population. On the other hand, there are operators with multiple offsprings, more than two descendants from two parents, which present a better behavior than the operators with only two descendants, and achieve a good balance between exploration and exploitation. © 2009 Wiley Periodicals, Inc. [source]

A novel steady-state genetic algorithm approach to the reliability optimization design problem of computer networks

INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT, Issue 1 2009
A. M. Mutawa
This paper introduces the development and implementation of a new methodology for optimizing reliability measures of a computer communication network within specified constraints. A genetic algorithm approach with specialized encoding, crossover, and mutation operators to design a layout topology optimizing source-terminal computer communication network reliability is presented. In this work, we apply crossover at the gene level in conjunction with the regular chromosome-level crossover operators that are usually applied on chromosomes or at boundaries of nodes. This approach provides us with a much better population mixture, and hence faster convergence and better reliability. Applying regular crossover and mutation operators on the population may generate infeasible chromosomes representing a network connection. This complicates fitness and cost calculations, since reliability and cost can only be calculated on links that actually exist. In this paper, a special crossover and mutation operator is applied in a way that will always ensure production of a feasible connected network topology. This results in a simplification of fitness calculations and produces a better population mixture that gives higher reliability rates at shorter convergence times. Copyright © 2008 John Wiley & Sons, Ltd. [source]