Average concept of crossover operator in real coded genetic algorithm
As the most important search operator in a Genetic Algorithm (GA) approach, many procedures have been proposed to accomplish the idea of a crossover.As a result, knowledge in crossover has incorporated special features such as statistical elements (i.e. arithmetic crossover) and natural observation...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IACSIT Press, Singapore.
2013
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/8318/1/Ross.pdf http://repo.uum.edu.my/8318/ http://dx.doi.org/10.7763/IPEDR |
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| Summary: | As the most important search operator in a Genetic Algorithm (GA) approach, many procedures have been proposed to accomplish the idea of a crossover.As a result, knowledge in crossover has incorporated special features such as statistical elements (i.e. arithmetic crossover) and natural observation (i.e. queen bee crossover) to name a few.Thus, this paper proposed a mean or average concept of crossover for finer parents to produce a new offspring in a GA based approach in an animal diet formulation problem.Experiments using real data were carried out involving GA models with average crossover and one-point crossover.Subsequently, the incorporation of power heuristics as a repair operator was investigated to find the best combination of ingredients, while removing the unwanted ones.Comparisons were made between GA models incorporating repair operator with different crossovers: average crossover and one point crossover.The results show that the performance of average crossover is comparable with that of the one point crossover.The inclusion of the repair operator provides an advantage that shows interesting solution for the tested problem. |
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