Biomedical

Combining Genetic Algorithm with Local Search Method in Solving Optimization Problems

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Velin Kralev,

Velin Kralev

Department of Informatics, Faculty of Mathematics and Natural Sciences, South-West University, 2700 Blagoevgrad, Bulgaria


Radoslava Kraleva

Radoslava Kraleva

Department of Informatics, Faculty of Mathematics and Natural Sciences, South-West University, 2700 Blagoevgrad, Bulgaria


  Peer Reviewed

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© attribution CC-BY

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2024-11-03

Doi: http://dx.doi.org/10.3390/electronics13204126
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Women and reproductive health

Abstract

This research is focused on evolutionary algorithms, with genetic and memetic algorithms discussed in more detail. A graph theory problem related to finding a minimal Hamiltonian cycle in a complete undirected graph (Travelling Salesman Problem—TSP) is considered. The implementations of two approximate algorithms for solving this problem, genetic and memetic, are presented. The main objective of this study is to determine the influence of the local search method versus the influence of the genetic crossover operator on the quality of the solutions generated by the memetic algorithm for the same input data. The results show that when the number of possible Hamiltonian cycles in a graph is increased, the memetic algorithm finds better solutions. The execution time of both algorithms is comparable. Also, the number of solutions that mutated during the execution of the genetic algorithm exceeds 50% of the total number of all solutions generated by the crossover operator. In the memetic algorithm, the number of solutions that mutate does not exceed 10% of the total number of all solutions generated by the crossover operator, summed with those of the local search method.

Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health
copyright icon

© attribution CC-BY

  • 0

rating
115 Views

Added on

2024-11-03

Doi: http://dx.doi.org/10.3390/electronics13204126
Related Subjects
Anatomy
Biochemistry
Epidemiology
Genetics
Neuroscience
Psychology
Oncology
Medicine
Musculoskeletal science
Pediatrics
Pathology
Pharmacology
Physiology
Psychiatry
Primary care
Women and reproductive health