Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolving neural networks through augmenting topologies
Evolutionary Computation
An empirical analysis of value function-based and policy search reinforcement learning
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Sustaining diversity using behavioral information distance
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
How novelty search escapes the deceptive trap of learning to learn
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Using behavioral exploration objectives to solve deceptive problems in neuro-evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Discovering several robot behaviors through speciation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
Autonomous Agents and Multi-Agent Systems
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Information Theory
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Evolving team behaviors with specialization
Genetic Programming and Evolvable Machines
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Niching schemes, which sustains population diversity and let an evolutionary population avoid premature convergence, have been extensively studied in the research field of evolutionary algorithms. Neuroevolutionary (NE) algorithms, such as NEAT, have also benefitted from niching. However, the latest research indicates that the use of genotype- or phenotype-similarity-based niching schemes in NE algorithms is not highly effective because these schemes have difficulty sustaining the behavioral diversity in the environment. In this paper, we propose a novel niching scheme that takes into consideration both the phenotypic and behavioral diversity, and then integrate it with NEAT. An experimental analysis revealed that the proposed algorithm outperforms the original NEAT for various problem settings. More interestingly, it performs especially well for problems with a high noise level and large state space. Since these features are common in problems to which NEAT is applied, the proposed algorithm should be effective in practice.