Robust Multi-Cellular Developmental Design
| Autor: | Marc Schoenauer, Nicolas Bredeche, Alexandre Devert |
|---|---|
| Přispěvatelé: | Algorithmic number theory for cryptology (TANC), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Laboratoire de Recherche en Informatique (LRI), Université Paris-Sud - Paris 11 (UP11)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS), ACM SIGEVO, D. Thierens et al., Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) |
| Jazyk: | angličtina |
| Rok vydání: | 2007 |
| Předmět: |
Artificial development
FOS: Computer and information sciences 0303 health sciences Mathematical optimization Artificial neural network Computer Science - Artificial Intelligence business.industry Continuous modelling Computer science Process (computing) Evolutionary algorithm 02 engineering and technology Embryogeny Phenotype Cellular automaton [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] 03 medical and health sciences Artificial Intelligence (cs.AI) Control theory Genotype 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business 030304 developmental biology |
| Zdroj: | Genetic and Evolutionary Computation COnference Genetic and Evolutionary Computation COnference, ACM SIGEVO, Jul 2007, London, United Kingdom. pp.982-989 GECCO |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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