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 complaints 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 hide all elements of man-made evolution: genetic programming, swarm intelligence, combinatorial and multi-objective optimization, idea in genetic algorithms and evolutionary structures, in addition to purposes 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
High speed obstacle avoidance using monocular vision and reinforcement learning. In: Proceedings of the 22nd international conference on Machine learning, pp. 593–600 (2005) 6. : Polly: A vision-based artiﬁcial agent. In: Proceedings of the Eleventh National Conference on Artiﬁcial Intelligence (AAAI-1993), pp. 824–829 (1993) 7. : Visually-guided obstacle avoidance in unstructured environments. In: Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robots and Systems, vol.
Iteration number. 5 (cf. Table 3), at a reasonable expense of more run time per metaheuristic, the hyperheuristic can clearly improve its overall eﬀectiveness. For ι = 2, 000, on average, the hyperheuristic produces 97 metaheuristics per second. To approach our question, we run the HH for ι = 15, 000. 85. On average, the GP hyperheuristic still produces 56 metaheuristics per second. 7 has more than tripled the overall eﬀectiveness of the hyperheuristic. 97) indicates reliable search behaviour. 6 Summary and Conclusions We have investigated our domain-independent, linear GP hyperheuristic (HH) [14, 13] with respect to its demand for computing resources in the context of its eﬀectiveness and eﬃciency.
The selection of a random unsatisﬁed clause which, eﬀectively, returns a list of variables). Generating SAT Local-Search Heuristics 41 After trying diﬀerent grammar representations we decided to design the grammar in such a way to produce nested functions to avoid using variables for passing data from a function to another. The aim was to reduce the constraints on the crossover and mutation operators, and to make the GP tree representing each individual simpler. The grammar we used and its components are shown in Figure 1.