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  • LCS Based Diversity Maintenance in Adaptive Genetic Algorithms

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    Islam207547-Accepted.pdf (664.5Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Ohira, R
    Islam, Md Saiful
    Jo, jun
    Stantic, B
    Griffith University Author(s)
    Islam, Saiful
    Ohira, Ryoma J.
    Jo, Jun
    Stantic, Bela
    Year published
    2019
    Metadata
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    Abstract
    A genetic algorithm (GA) experiences premature convergence when the diversity is lost in the population. Adaptive GAs aim to maintain diversity in the population by trading off a balance between exploring the problem space and exploiting known solutions. Existing metrics for population diversity measures only examine the similarity between individuals on a genetic level. However, similarities in the order of genes in individuals in ordered problems, such as the travelling salesman problem (TSP) can play an important role in effective diversity measures. By examining the similarities of individuals by the order of their genes, ...
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    A genetic algorithm (GA) experiences premature convergence when the diversity is lost in the population. Adaptive GAs aim to maintain diversity in the population by trading off a balance between exploring the problem space and exploiting known solutions. Existing metrics for population diversity measures only examine the similarity between individuals on a genetic level. However, similarities in the order of genes in individuals in ordered problems, such as the travelling salesman problem (TSP) can play an important role in effective diversity measures. By examining the similarities of individuals by the order of their genes, this paper proposes longest common subsequence (LCS) based metrics for measuring population diversity and its application in adaptive GAs for solving TSP. Extensive experimental results demonstrate the superiority of our proposal to existing approaches.
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    Conference Title
    Communications in Computer and Information Science
    Volume
    996
    DOI
    https://doi.org/10.1007/978-981-13-6661-1_5
    Copyright Statement
    © Springer Nature Singapore Pte Ltd. 2019. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher.The original publication is available at www.springerlink.com
    Subject
    Optimisation
    Computational complexity and computability
    Artificial life and complex adaptive systems
    Publication URI
    http://hdl.handle.net/10072/392633
    Collection
    • Conference outputs

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