Accelerating genetic schema processing through local search

Tarek A. El-Mihoub, Adrian Hopgood, Ibrahim A. Aref

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    156 Downloads (Pure)


    Achieving a balance between the exploration and exploitation capabilities of genetic algorithms is a key factor for their success in solving complicated search problems. Incorporating a local search method within a genetic algorithm can enhance the exploitation of local knowledge but it risks decelerating the schema building process. This paper defines some features of a local search method that might improve the balance between exploration and exploitation of genetic algorithms. Based on these features a probabilistic local search method is proposed. The proposed search method has been tested as a secondary method within a staged hybrid genetic algorithm and as a standalone method. The experiments conducted showed that the proposed method can speed up the search without affecting the schema processing of genetic algorithms. The experiments also showed that the proposed algorithm as a standalone algorithm can, in some cases, outperform a pure genetic algorithm.

    Original languageEnglish
    Title of host publicationProceeding - 2013 International Conference on Computer, Control, Informatics and Its Applications
    Subtitle of host publication"Recent Challenges in Computer, Control and Informatics", IC3INA 2013
    PublisherIEEE Computer Society
    Number of pages6
    ISBN (Print)9781479910786
    Publication statusPublished - 2013
    Event2013 International Conference on Computer, Control, Information and Its Applications - Jakarta, Indonesia
    Duration: 19 Nov 201321 Nov 2013


    Conference2013 International Conference on Computer, Control, Information and Its Applications
    Abbreviated titleIC3INA 2013


    • hybrid genetic algorithm
    • Lamarckian learning
    • Lamarckian search
    • local search
    • memetic search
    • schema processing


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