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This publication constitutes the completely refereed post-conference court cases of the eighth foreign convention on synthetic 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 conceal all features of synthetic evolution: genetic programming, swarm intelligence, combinatorial and multi-objective optimization, thought in genetic algorithms and evolutionary structures, 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
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. For example, in recent work  genetic programming [13,14] was successfully used to evolve new heuristics in HH-Class 3 for one-dimensional online bin packing problems. Very positive results with evolving oﬄine bin-packing heuristics have recently been obtained in  where GP was used to evolve strategies to guide a ﬁxed solver.
G. for the detection of interest points [13,14]. Ebner also developed a navigation system for a robot using range sensors  but he didn’t combine the two approaches for a visionbased navigation system. Parisian evolution has also been shown to produce very good results for obstacle detection and 3D reconstruction but those systems need two calibrated cameras [16,17]. To our knowledge, only Martin tried evolutionary techniques with monocular images for obstacle avoidance . The structure of his algorithm is based on Automatic Design of Vision-Based Obstacle Avoidance Controllers 27 the ﬂoor segmentation technique and the evaluation is done with a database of hand labeled real world image.
Automatic Design of Vision-Based Obstacle Avoidance Controllers 29 – Scalar operators (input: scalar(s), output: scalar ): Addition, subtraction, multiplication and division operators, temporal mean calculation and simple if-then-else test. – Command generation (input: scalar(s), output: command ): The motor command is represented by two scalar values: the requested linear and angular speeds. We created two operators to generate these values: • Direct generation: The required linear and angular speeds are two input scalar values.