By Luis Da Costa, Jacques-André Landry, Yan Levasseur (auth.), Nicolas Monmarché, El-Ghazali Talbi, Pierre Collet, Marc Schoenauer, Evelyne Lutton (eds.)
This booklet constitutes the completely refereed post-conference lawsuits of the eighth foreign convention on man made Evolution, EA 2007, held in excursions, France in October 2007.
The 27 revised complete papers awarded have been conscientiously reviewed and chosen from sixty two submissions in the course of rounds of reviewing and development. The papers hide all elements of man-made evolution: genetic programming, swarm intelligence, combinatorial and multi-objective optimization, conception in genetic algorithms and evolutionary platforms, in addition to purposes of evolutionary algorithms.
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Additional resources for Artificial Evolution: 8th International Conference, Evolution Artificielle, EA 2007, Tours, France, October 29-31, 2007, Revised Selected Papers
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In: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 887–894 (2006) 15. : Evolving a behavior-based control architecture-From simulations to the real world. In: Proceedings of Genetic and Evolutionary Computation Conference, vol. 2, pp. 1009–1014 (1999) 16. : 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.
We proceed then to a second run with a different starting point and a different goal location. Scores G2 and C2 are calculated the same way and the total scores GT and CT are obtained by adding the scores from the two runs: GT = G1 + G2 CT = C1 + C2 The goal is hence to minimize those two scores GT and CT . Performing two different runs favors algorithms with a real obstacle avoidance strategy while not increasing evaluation time too much. The starting points are fixed because we want to evaluate all algorithms on the same problem.
This time, almost all the evolved controllers use direct integral computations on the image to detect obstacles. When approaching the bookshelves, the bottom of the shelves becomes more visible. Those areas are darker because the light comes from the ceiling in these experiments, thus with a simple integral computation in the upper part of the image, the robot brakes when approaching an obstacle. This obstacle detection technique is coupled with a target heading behavior and a seemingly random back and forth move that partially prevents the robot from getting stuck behind large obstacles.