2002•Unpublished venueRequires access

Improved crossover strategy of genetic algorithms and analysis of its performance

Bian Runqiang, Chen Zengqiang, Yuan Zhuzhi

Abstract

In this paper, the premature convergence problem of genetic algorithms is analyzed from a point of view of the crossover efficiency, and a new crossover strategy is proposed to make the crossover more efficient. The strategy is effective in preventing incest and overcoming the premature convergence.

About this research paper

What this paper is about

Why it matters

OpenAlex reports 7 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Key concepts: Crossover, Premature convergence, Convergence (economics), Computer science, Genetic algorithm, Algorithm, Mathematical optimization, Point (geometry)