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A gestalt genetic algorithm: less details for better search

Published: 07 July 2007 Publication History

Abstract

The basic idea to defend in this paper is that an adequate perception of the search space, sacrificing most of the precision, can paradoxically accelerate the discovery of the most promising solution zones. While any search space can be observed at any scale according to the level of details, there is nothing inherent to the classical metaheuristics to naturally account for this multi-scaling. Nevertheless, the wider the search space the longer the time needed by any metaheuristic to discover and exploit the "promising" zones. Any possibility to compress this time is welcome. Abstracting the search space during the search is one such possibility. For instance, a common Ordering Genetic Algorithm (o-GA) is not well suited to efficiently resolve very large instances of the Traveling Salesman Problem (TSP). The mechanism presented here (reminiscent of Gestalt psychology) aims at abstracting the search space by substituting the variables of the problems with macro-versions of them. This substitution allows any given metaheuristic to tackle the problem at various scales or through different multi-resolution lenses. In the TSP problem to be treated here, the towns will simply be aggregated into regions and the metaheuristics will apply on this new one-level-up search space. The whole problem becomes now how to discover the most appropriate regions and to merge this discovery with the running of the o-GA at the new level.

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 July 2007

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    Author Tags

    1. genetic algorithm
    2. gestalt
    3. traveling salesman problem

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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    • (2012)The Gestalt heuristicNatural Computing: an international journal10.1007/s11047-012-9317-x11:3(499-517)Online publication date: 1-Sep-2012
    • (2011)Symbiosis Enables the Evolution of Rare Complexes in Structured EnvironmentsAdvances in Artificial Life. Darwin Meets von Neumann10.1007/978-3-642-21314-4_14(110-117)Online publication date: 2011
    • (2010)Adopting co-evolution and constraint-satisfaction concept on genetic algorithms to solve supply chain network design problemsExpert Systems with Applications: An International Journal10.1016/j.eswa.2010.03.03037:10(6919-6930)Online publication date: 1-Oct-2010
    • (2009)Symbiosis enables the evolution of rare complexes in structured environmentsProceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II10.5555/2017762.2017778(110-117)Online publication date: 13-Sep-2009
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