Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Evolving neural networks through augmenting topologies
Evolutionary Computation
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
ALPS: the age-layered population structure for reducing the problem of premature convergence
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Complex networks of simple neurons for bipedal locomotion
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Abandoning objectives: Evolution through the search for novelty alone
Evolutionary Computation
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Evolution of central pattern generators for bipedal walking in areal-time physics environment
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Real-time neuroevolution in the NERO video game
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Diversity maintenance techniques in evolutionary computation are designed to mitigate the problem of deceptive local optima by encouraging exploration. However, as problems become more difficult, the heuristic of fitness may become increasingly uninformative. Thus, simply encouraging genotypic diversity may fail to much increase the likelihood of evolving a solution. In such cases, diversity needs to be directed towards potentially useful structures. A representative example of such a search process is novelty search, which builds diversity by rewarding behavioral novelty. In this paper the effectiveness of fitness, novelty, and diversity maintenance objectives are compared in two evolutionary robotics domains. In a biped locomotion domain, genotypic diversity maintenance helps evolve biped control policies that travel farther before falling. However, the best method is to optimize a fitness objective and a behavioral novelty objective together. In the more deceptive maze navigation domain, diversity maintenance is ineffective while a novelty objective still increases performance. The conclusion is that while genotypic diversity maintenance works in well-posed domains, a method more directed by phenotypic information, like novelty search, is necessary for highly deceptive ones.