Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Guest editorial: special issue on evolutionary multiobjective optimization
IEEE Transactions on Evolutionary Computation
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This paper addresses the problem of automatic parallel parking by a back-wheel drive vehicle, using a biomimetic model based on direct coupling between vehicle perceptions and actions. This problem is solved by means of a bio-inspired approach in which the vehicle controller does not need to know the car kinematics and dynamics, neither does it call for a priori knowledge of the environment map. The key point in the proposed approach is the definition of performance indices that for automatic parking happen to be functions of the strategic orientations to be injected, in real time, to the car-like robot controller. This solution leads to a dynamic multi-objective optimization problem, which is extremely hard to be dealt with analytically. A genetic algorithm is therefore applied, thanks to which we obtain a very simple and efficient solution. The paper ends with the results of computer simulations.