Reducing Local Optima in Single-Objective Problems by Multi-objectivization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Incremental Evolution of Animats' Behaviors as a Multi-objective Optimization
SAB '08 Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats
Fitness functions in evolutionary robotics: A survey and analysis
Robotics and Autonomous Systems
Why Coevolution Doesn't "Work": Superiority and Progress in Coevolution
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Multi-objective evolution of robot neuro-controllers
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Co-evolving complex robot behavior
ICES'03 Proceedings of the 5th international conference on Evolvable systems: from biology to hardware
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Evolving counter-propagation neuro-controllers for multi-objective robot navigation
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
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Aggregate fitness selection is known to suffer from the bootstrap problem, which is often viewed as the main inhibitor of the widespread application of aggregate fitness selection in evolutionary robotics. There remains a need to identify methods that overcome it, while requiring the minimum amount of a priori task knowledge from the designer. We suggest a novel two-phase method. In the first phase, it exploits multi objective optimization to develop a population of controllers that exhibit several desirable behaviors. In the second phase, it applies aggregate selection using the previously obtained population as the seed. The method is assessed by two non-traditional comparison procedures. The proposed approach is demonstrated using simulated coevolution of two robotic soccer players. The multi objective phase is based on adaptation of the well-known NSGA-II algorithm for coevolution. The results demonstrate the potential advantage of the suggested two-phase approach over the conventional one.