Paper Details
Subject:
Paper ID: UIJRTV4I70035
Volume:04
Issue:07
Pages:303-309
Date:May 2023
ISSN:2582-6832
Statistics:

Loading

  Full Text [PDF]

Cite this
Jose Antonio M. Aguila, Loise Gabriel C. Gitalado, Vivien A Agustin, Richard C. Regala and Mark Christopher R. Blanco, 2023. Further Enhancement of Genetic Algorithm Using Multiple Crossover and Mutation Operators. United International Journal for Research & Technology (UIJRT), 4(7), pp303-309
Abstract
This research focuses on further enhancing the performance of genetic algorithms through the utilization of multiple crossover and mutation operators. The primary objectives of this study are to improve the effectiveness of genetic algorithms by employing diverse crossover and mutation operators, develop a novel crossover operator to overcome limitations of existing operators, and propose innovative crossover and mutation operators to address local optima issue. The methodology employed in this research centers around the Nurse Scheduling Problem, which involves creating an optimal schedule for a group of nurses considering various constraints. Genetic algorithms are used to solve this problem, with binary encoding representing the nurse schedule. To enhance the algorithm, three crossover algorithms (Poor and Rich Optimization, Golden Search Algorithm, and Prairie Dog Optimization Algorithm) and three mutation algorithms (Chaotic Vortex Search Algorithm, Capuchin Search Algorithm, and Chaos Cloud Quantum Bat Hybrid Optimization) are proposed. The research findings indicate that the combination of Prairie Dog and Capuchin Search algorithms outperforms other combinations, resulting in a significantly lower number of shift violations (only 7 violations). Overall, this research contributes to the field of genetic algorithms by presenting novel crossover and mutation operators that enhance their performance, particularly in tackling the Nurse Scheduling Problem.

Keywords: Genetic Algorithm, Crossover, Mutation, Capuchin Search algorithm, Chaos Cloud Quantum Bat Hybrid Optimization, Chaotic Vortex Search algorithm, Golden Search Optimization algorithm, Poor and Rich Optimization algorithm, Prairie Dog Optimization algorithm.


Related Papers

Close Menu