How an optimal observer can smooth a landscape

Philemotte, Christophe; Bersini, Hugues

doi:10.1109/cec.2006.1688591

Cited by 1 publication

(6 citation statements)

References 18 publications

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“…In [19,20], the authors have suggested an algorithm to improve the exploration process of a Simple GA (SGA). This algorithm allows the presence of masks to hide many details of the search space so as to accelerate the discovery of promising solution zones.…”

Section: A Gestalt Type Of Enhancementmentioning

confidence: 99%

“…The search over the definition of the lenses is guided by evaluations provided by the search executed using these lenses over the original search space. In [19,20], the Observer mainly supervises the given metaheuristic and always restarts the experiments from the same observation. In this paper, the Observer mechanism acts all along the given metaheuristic searches for good candidate solutions and at fixed time intervals.…”

Section: A Gestalt Type Of Enhancementmentioning

confidence: 99%

“…Intrinsic Emergence and Gestalt psychology are very related concepts. Some authors [2,18,19,20] have proposed a concrete interpretation of this concept in the EC framework: alterating of the perception of the search space in a useful way. It is done by considering only some of the details of the search space and by creating indivisible macroscopic genes, a "Gestalt".…”

Section: Introductionmentioning

confidence: 99%

“…In EC, several works have be done in this conceptual vein [2,18,19,20,7,6,26]. In this paper, we aim at developing a new version of the Observer Algorithm ( [18,19,20]) but still persisting with this same conceptual framework.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we aim at developing a new version of the Observer Algorithm ( [18,19,20]) but still persisting with this same conceptual framework. In particular, we will apply it to the TSP which is indeed a very well suited application to illustrate the whole idea.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A gestalt genetic algorithm

Philemotte

Bersini

2007

Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation

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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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Section: A Gestalt Type Of Enhancementmentioning

confidence: 99%

Section: A Gestalt Type Of Enhancementmentioning

confidence: 99%