Download Artificial Evolution: 8th International Conference, by Luis Da Costa, Jacques-André Landry, Yan Levasseur (auth.), PDF

By Luis Da Costa, Jacques-André Landry, Yan Levasseur (auth.), Nicolas Monmarché, El-Ghazali Talbi, Pierre Collet, Marc Schoenauer, Evelyne Lutton (eds.)

This ebook constitutes the completely refereed post-conference lawsuits of the eighth overseas convention on synthetic Evolution, EA 2007, held in excursions, France in October 2007.

The 27 revised complete papers provided have been rigorously reviewed and chosen from sixty two submissions in the course of rounds of reviewing and development. The papers conceal all facets of man-made evolution: genetic programming, swarm intelligence, combinatorial and multi-objective optimization, idea in genetic algorithms and evolutionary structures, in addition to functions of evolutionary algorithms.

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Additional info for Artificial Evolution: 8th International Conference, Evolution Artificielle, EA 2007, Tours, France, October 29-31, 2007, Revised Selected Papers

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There a genetic algorithm was used to generate offspring solutions to SAT using the standard genetic operators. However, offspring were then improved by means of local search methods. The same authors later proposed, ASAP, a variant of FlipGA [17]. A good overview of other algorithms of this type is provided in [12]. The second direction, which we also adopt in this paper, is to use evolutionary techniques to automatically evolve local search heuristics. A successful example of this is the CLASS system developed by Fukunaga [9,10].

We focus on the obstacle avoidance problem as it is a necessary function for a mobile robot. As a first step, we use an offline procedure to automatically design algorithms adapted to the visual context. This procedure is based on genetic programming and the candidate algorithms are evaluated in a simulation environment. The evolutionary process selects meaningful visual primitives in the given context and an adapted strategy to use them. The results show the emergence of several different behaviors outperforming hand-designed controllers.

Evolutionary Optimisation for Obstacle Detection and Avoidance in Mobile Robotics. Journal of Advanced Computational Intelligence and Intelligent Informatics 9(6), 622–629 (2005) 17. : Parisian evolution with honeybees for three-dimensional reconstruction. In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 191–198 (2006) 18. : Evolving visual sonar: Depth from monocular images. Pattern Recognition Letters 27(11), 1174–1180 (2006) 19. : Performance of optical flow techniques.

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