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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
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 . A good overview of other algorithms of this type is provided in . 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.