Intrinsic emergence boosts adaptive capacity

Philemotte, Christophe; Bersini, Hugues

doi:10.1145/1068009.1068103

Cited by 3 publications

(6 citation statements)

References 2 publications

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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%

“…Its main goal consists in improving the basic SGA exploration process. The principle is based on the evolution of observers which alter the way the SGA looks at the landscape and travels through it [15,14]. This alteration changes the encoding by reducing and smoothing the search space to more informative subsets.…”

Section: Resultsmentioning

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: 99%

“…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: Resultsmentioning

confidence: 99%

Section: The Addition Of Competitive Observers On Sgamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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How an optimal observer can smooth a landscape

Philemotte

Bersini

2006 IEEE International Conference on Evolutionary Computation

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Emergence in Agent-Based Computational Social Science

Ferber¹,

Dessalles

Phan

2008

Intelligent Complex Adaptive Systems

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This chapter provides a critical survey of emergence definitions both from a conceptual and formal standpoint. The notions of downward/backward causation and weak/strong emergence are specially discussed for application to complex social system with cognitive agents. Particular attention is devoted to the formal definitions introduced by Müller (2004) and Bonabeau and Dessalles (1997), which are operative in multi-agent frameworks and make sense from both cognitive and social point of view. A diagrammatic 4-Quadrant approach allows us to understand complex phenomena along both interior/exterior and individual/collective dimensions.

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Intrinsic emergence boosts adaptive capacity

Cited by 3 publications

References 2 publications

How an optimal observer can collapse the search space

How an optimal observer can collapse the search space

How an optimal observer can smooth a landscape

Emergence in Agent-Based Computational Social Science

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