A Study on Population’s Diversity for Dynamic Environments

Simões, Anabela; Carvalho, Rui A.; Campos, Joao R.; Costa, Ernesto

doi:10.1007/978-3-642-20282-7_31

Cited by 2 publications

(1 citation statement)

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“…This assumption was proved, but a side effect was observed: the diversity of the population decreased and a high number of individuals presented similar genotypes. As shown in [6], when using memory, usually, a lower population's diversity does not affect the performance of the EA because the memorized solutions are very effective for this type of environments. For random environments memory is not so effective and consequently extra diversity in the population is required.…”

Section: Analyzing the Influence Of βmentioning

confidence: 98%

Enhancing the virtual loser genetic algorithm for dynamic environments

Simões

Costa

2012

Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation

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In recent years, several memory schemes have been used in Evolutionary Algorithms (EAs) for dynamic optimization problems (DOPs). The Virtual Loser Genetic Algorithm (VLGA), recently proposed, uses a novel type of associative memory to deal with DOPs. This memory scheme memorizes past errors concerning the performed mutations and uses this information to create new individuals when a change in the environment occurs. In this paper the VLGA is further investigated in order to enhance its performance in different types of DOPs: the influence of an important parameter is analyzed, and the interaction between the memory scheme and the use of immigrants is also investigated. A novel immigrant scheme is proposed and compared with the random immigrants approach. The results show that the investigated methods significantly enhance the previous version of the VLGA for cyclic and random environments.

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Section: Analyzing the Influence Of βmentioning

confidence: 98%