ScholarLens Back to search
2001•Chinese Journal of ComputersRequires access

Good Point Set Based Genetic Algorithm

Ling Zhang

Open publisher page 61 citations

Abstract

By analyzing the genetic algorithm(GA) based on its idea density model, the essence and characteristics of GA are given. It is shown that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness. Based on the results, the crossover operation in GA is redesigned by using the principle of good point set in number theory. Then a new GA called good point set based GA is presented. The new GA is applied to optimization problems such as SAT,TSP, etc. Compared to other approaches for solving SAT, the simulation results show that the new GA has superiority in speed, accuracy and overcoming premature. The new interpretation of GA and the proposed good point set based GA provide a new way for investigating GA.

About this research paper

What this paper is about

By analyzing the genetic algorithm(GA) based on its idea density model, the essence and characteristics of GA are given. It is shown that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness. Based on the results, the crossover operation in GA is redesigned by using the principle of good point set in number theory. Then a new GA called good point set based GA is presented. The new GA is applied to optimization problems such as SAT,TSP, etc. Compared to other approaches for solving SAT, the simulation results show that the new GA has superiority in speed, accuracy and overcoming premature. The new interpretation of GA and the proposed good point set based GA provide a new way for investigating GA.

Why it matters

OpenAlex reports 61 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

By analyzing the genetic algorithm(GA) based on its idea density model, the essence and characteristics of GA are given. It is shown that the GA is a guided random search and the guiding direction always aims at the family whose ancestors have schemata with high fitness. Based on the results, the crossover operation in GA is redesigned by using the principle of good point set in number theory. Then a new GA called good point set based GA is presented. The new GA is applied to optimization problems such as SAT,TSP, etc. Compared to other approaches for solving SAT, the simulation results show that the new GA has superiority in speed, accuracy and overcoming premature. The new interpretation of GA and the proposed good point set based GA provide a new way for investigating GA.

Key concepts: Crossover, Set (abstract data type), Point (geometry), Genetic algorithm, Algorithm, Interpretation (philosophy), Computer science, Mathematical optimization

Related papers

Related paper 1Related paper 2Related paper 3
Back to paper searchBrowse research topicsOriginal source
Good Point Set Based Genetic Algorithm — Research Paper | ScholarLens