By Luis Da Costa, Jacques-André Landry, Yan Levasseur (auth.), Nicolas Monmarché, El-Ghazali Talbi, Pierre Collet, Marc Schoenauer, Evelyne Lutton (eds.)
This publication 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 offered have been rigorously reviewed and chosen from sixty two submissions in the course of rounds of reviewing and development. The papers conceal all elements of man-made evolution: genetic programming, swarm intelligence, combinatorial and multi-objective optimization, concept in genetic algorithms and evolutionary platforms, in addition to functions of evolutionary algorithms.
Read Online or Download Artificial Evolution: 8th International Conference, Evolution Artificielle, EA 2007, Tours, France, October 29-31, 2007, Revised Selected Papers PDF
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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
Individuals that have not been eﬀected by any genetic operator are not evaluated again to reduce the computation cost of the evolution phase. Figure 3 shows how the framework works and how the interaction between the two main engines, GP and SAT, operates. As we mentioned before, we apply GP-HH to discover high-quality SAT solvers specialised for SAT instances of a particular size. So, we pass to the system sets of SAT instances all with the same number of variables.
Nevertheless detecting obstacles with a single camera is a diﬃcult problem. Diﬀerent solutions exist, using either optical ﬂow or more simple contextual information, but none is generic enough to deal with changing environments. Ideally, the robot should be able to adapt itself dynamically to the current context and to select its behavior (here, the obstacle avoidance algorithm) in real time depending on its environment. The robot would learn those most eﬃcient behaviors only by interacting with the environment.
Bader-El-Den and R. Poli connected with one another is lost. To avoid this problem, this information is captured by a grammar. So, in order to provide the hyper-heuristic systems with enough information on how to use components to create valid heuristics, one must ﬁrst construct an appropriate grammar. Hence, in the hyper-heuristics in HH-Class 2, both the grammar and the heuristics components are given to the hyper-heuristic systems. The system then uses a suitable evolutionary algorithm to evolve new heuristics.