Robust Multi-Cellular Developmental Design

Alexandre Devert 1 Nicolas Bredeche 1, 2 Marc Schoenauer 1
1 TANC - Algorithmic number theory for cryptology
LIX - Laboratoire d'informatique de l'École polytechnique [Palaiseau], Inria Saclay - Ile de France, Polytechnique - X, CNRS - Centre National de la Recherche Scientifique : UMR7161
Abstract : This paper introduces a continuous model for Multi-cellular Developmental Design. The cells are fixed on a 2D grid and exchange "chemicals" with their neighbors during the growth process. The quantity of chemicals that a cell produces, as well as the differentiation value of the cell in the phenotype, are controlled by a Neural Network (the genotype) that takes as inputs the chemicals produced by the neighboring cells at the previous time step. In the proposed model, the number of iterations of the growth process is not pre-determined, but emerges during evolution: only organisms for which the growth process stabilizes give a phenotype (the stable state), others are declared nonviable. The optimization of the controller is done using the NEAT algorithm, that optimizes both the topology and the weights of the Neural Networks. Though each cell only receives local information from its neighbors, the experimental results of the proposed approach on the 'flags' problems (the phenotype must match a given 2D pattern) are almost as good as those of a direct regression approach using the same model with global information. Moreover, the resulting multi-cellular organisms exhibit almost perfect self-healing characteristics.
keyword : Embryogeny
Type de document :
Communication dans un congrès
D. Thierens et al. Genetic and Evolutionary Computation COnference, Jul 2007, London, United Kingdom. ACM Press, pp.982-989, 2007
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Soumis le : mercredi 9 mai 2007 - 16:25:21
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14
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  • HAL Id : inria-00145336, version 1
  • ARXIV : 0705.1309

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Alexandre Devert, Nicolas Bredeche, Marc Schoenauer. Robust Multi-Cellular Developmental Design. D. Thierens et al. Genetic and Evolutionary Computation COnference, Jul 2007, London, United Kingdom. ACM Press, pp.982-989, 2007. 〈inria-00145336〉

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