This paper presents an empirical study of population diversity measures and adaptive control of diversity in the context of a permutation-based algorithm for Traveling Salesman Problems and Vehicle Routing Problems. We provide detailed graphical observations and discussion of the relationship among the four diversity measures and suggest a moderate correlation between diversity and search performance under simple conditions. We also study the effects of adapting key genetic control parameters such as crossover and mutation rates on the population diversity. We are able to show that adaptive control of the genetic operations based on population diversity effectively outperforms fixed parameter genetic algorithms.
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- Population Diversity in Permutation-Based Genetic Algorithm
Kenny Q. Zhu
- Springer Berlin Heidelberg
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