Developing an Adaptation Process for Real-Coded Genetic Algorithms


Saraçoğlu R., Kazankaya A. F.

COMPUTER SYSTEMS SCIENCE AND ENGINEERING, vol.35, no.1, pp.13-19, 2020 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 35 Issue: 1
  • Publication Date: 2020
  • Journal Name: COMPUTER SYSTEMS SCIENCE AND ENGINEERING
  • Journal Indexes: Science Citation Index Expanded, Scopus, PASCAL, Aerospace Database, Applied Science & Technology Source, Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.13-19
  • Keywords: Adaptive algorithms, Algorithm design and analysis, Genetic algorithms, Value coding genetic algorithms, OPTIMIZATION, CROSSOVER, PROBABILITIES, FLEXIBILITY, OPERATORS, EVOLUTION, MUTATION

Abstract

The genetic algorithm (GA) is a metaheuristic method which simulates the life cycle and the survival of the fittest in the nature for solving optimization problems. This study aimed to develop enhanced operation by modifying the current GA. This development process includes an adaptation method that contains certain developments and adds a new process to the classic algorithm. Individuals of a population will be trialed to adapt to the current solution of the problem by taking them separately for each generation. With this adaptation method, it is more likely to get better results in a shorter time. Experimental results show that this new process accelerated the algorithm and a certain solution has been reached in fewer generations. In addition, better solutions were achieved, especially for a certain number of generations.