Open AccessAnalysis and comparison of a proposed mutation operator and its effects on the performance of genetic algorithm
Author(s) -
Sami Ullah,
Abdus Salam,
Mohsin Masood
Publication year - 2022
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v25.i2.pp1208-1216
Subject(s) - crossover , operator (biology) , mutation , genetic operator , selection (genetic algorithm) , population , genetic algorithm , swap (finance) , mathematical optimization , mathematics , computer science , convergence (economics) , algorithm , artificial intelligence , population based incremental learning , genetics , biology , demography , finance , repressor , sociology , transcription factor , economics , gene , economic growth
Genetic algorithms (GAs) are dependent on various operators and parameters. The most common evolutionary operators are parent selection, crossover, and mutation. Each operator has broad implementations with its pros and cons. A successful GA is highly dependent on genetic diversity which is the main driving force that steers a GA towards an optimal solution. Mutation operator implements the idea of exploration to search for uncharted areas and introduces diversity in a population. Thus, increasing the probability of GA to converge to a globally optimum solution. In this paper, a new variant of mutation operator is proposed, and its functions are studied and compared with the existing operators. The proposed mutation operator as well as others such as m-mutation, shuffle, swap, and inverse are tested for their ability to introduce diversity in population and hence, their effects on the performance of GA. All these operators are applied to Max one problem. The results concluded that the proposed variant is far more superior to the existing operators in terms of introducing diversity and hence early convergence to an optimum solution.