Introduction to AI Robotics
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Computation - Special issue on magnetic algorithms
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Multiobjective Evolution of Neural Controllers and Task Complexity
IEEE Transactions on Robotics
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Bootstrapping aggregate fitness selection with evolutionary multi-objective optimization
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Evolving counter-propagation neuro-controllers for multi-objective robot navigation
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
Multi-objective evolutionary design of robust controllers on the grid
Engineering Applications of Artificial Intelligence
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This paper concerns a non-traditional evolutionary robotics approach to robot navigation. Navigation is presented as a problem of two conflicting objectives. The first concerns a classical "amalgamated" objective, which has been traditionally used to increase speed, move straight as possible, and at the same time avoid obstacles. The second objective is devised to simultaneously encourage a sequential acquisition of targets. To solve the presented problem a modification of the well known NSGA-II algorithm has been performed. The proposed approach is tested using a simulation of a Khepera. The study sheds light on different aspects of the aforementioned problem and on the applicability of evolutionary multi-objective optimization to the simultaneous learning of a variety of controllers for deferent behaviors. Finally, based on this initial study, future work is suggested, which may allow to shift such multi-objective evolutionary studies from toy problems to more realistic situations.