CoEvolution of Effective Observers and Observed Multi-agents System

Philemotte, Christophe; Bersini, Hugues

doi:10.1007/11553090_79

Cited by 5 publications

(11 citation statements)

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“…Starting from original works discussed in [16,3,4,1,15,14], we propose a new approach called EO-SGA. Its main goal consists in improving the basic SGA exploration process.…”

Section: Resultsmentioning

confidence: 99%

“…The current implementation of this search is again based on a SGA (see Algorithm 2) transforming the whole algorithm as an intertwining of two evolutionary stages (through Evaluate(...)). The Evaluate(...) function corresponds to the EvaluateMetaheuristicRunWithObserver(...) and the observer evaluation function is then defined by [14]:…”

Section: The Addition Of Competitive Observers On Sgamentioning

confidence: 99%

“…In a preliminary attempt to evolve Cellular Automata, the observers were generated in a random and unguided way [1]. In a successive attempt, an evolutionary search was proposed and tested [15,14]. Here we follow this second evolutionary search in the space of the observers.…”

Section: The Addition Of Competitive Observers On Sgamentioning

confidence: 99%

“…The notion of Intrinsic Emergence (IE), which was originally inspired by the developments of Crutchfield and Mitchell, appears to be very helpful here [16,3,4,1,15,14,8,7]. According to these authors, there are mechanical ways to observe a complex system so as to provide this mechanical observer with added and profitable functionalities.…”

Section: Introductionmentioning

confidence: 98%

“…While previous works aims at a conceptual introduction of the approach and a rough illustration by an evolutionary optimisation of a Cellular Automaton [15,14], this paper attempts a generalisation to all metaheuristics with special focus on evolutionary ones. To test this generalisation, the problem to be tackled is one instance of Shuffled HIFF (SHIFF) [20,21,19,5].…”

Section: Introductionmentioning

confidence: 99%

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How an optimal observer can collapse the search space

Philemotte

Bersini

2006

Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation

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Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search space. For instance, the Simple Genetic Algorithm (SGA) is not totally suited to traverse very large landscapes, especially deceptive ones. The approach introduced here aims at improving the exploration process of the SGA by adding a second search process through the way the solutions are coded. An "observer" is defined as each possible encoding that aims at reducing the search space. Adequacy of one observer is computed by applying this specific encoding and evaluating how this observer is beneficial for the SGA run. The observers are trained for a specific time by a second evolutionary stage. During the evolution of the observers, the most suitable observer helps the SGA to find a solution to the tackled problem faster. These observers aim at collapsing the search space and smoothing its ruggedness through a simplification of the genotype. A first implementation of this general approach is proposed, tested on the Shuffled Hierarchical IF-and-only-iF (SHIFF) problem. Very good results are obtained and some explanations are provided about why our approach tackles SHIFF so easily.

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“…Starting from original works discussed in [16,3,4,1,15,14], we propose a new approach called EO-SGA. Its main goal consists in improving the basic SGA exploration process.…”

Section: Resultsmentioning

confidence: 99%

Section: The Addition Of Competitive Observers On Sgamentioning

confidence: 99%

Section: The Addition Of Competitive Observers On Sgamentioning

confidence: 99%