Comparative analysis of genetic crossover operators in knapsack problem
| Autor: | David Opeoluwa Oyewola, Y. Yahaya, Gbolahan Bolarin, D. Hakimi |
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| Rok vydání: | 2016 |
| Předmět: |
Condensed Matter::Quantum Gases
Mathematical optimization Heuristic (computer science) Computer Science::Neural and Evolutionary Computation Crossover Continuous knapsack problem MathematicsofComputing_NUMERICALANALYSIS Evolutionary algorithm ComputingMethodologies_ARTIFICIALINTELLIGENCE Knapsack problem TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITY Convergence (routing) Genetic algorithm Genetic Algorithm Crossover Heuristic Arithmetic Intermediate Evolutionary Algorithm Point (geometry) MathematicsofComputing_DISCRETEMATHEMATICS Mathematics |
| Zdroj: | Journal of Applied Sciences and Environmental Management; Vol 20, No 3 (2016); 593-596 |
| ISSN: | 1119-8362 |
| DOI: | 10.4314/jasem.v20i3.13 |
| Popis: | The Genetic Algorithm (GA) is an evolutionary algorithms and technique based on natural selections of individuals called chromosomes. In this paper, a method for solving Knapsack problem via GA (Genetic Algorithm) is presented. We compared six different crossovers: Crossover single point, Crossover Two point, Crossover Scattered, Crossover Heuristic, Crossover Arithmetic and Crossover Intermediate. Three different dimensions of knapsack problems are used to test the convergence of knapsack problem. Based on our experimental results, two point crossovers (TP) emerged the best result to solve knapsack problem. Keywords: Genetic Algorithm, Crossover, Heuristic, Arithmetic, Intermediate, Evolutionary Algorithm |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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