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
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective Pareto genetic algorithms using fast elite updating
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
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This paper examines the use of fitness sharing in evolutionary multi-objective optimization (EMO) algorithms to form a uniform distribution of niches along the non-dominated frontier. A long-standing, implicit assumption is that fitness sharing within an equivalence class, such as the Pareto optimal set, can form dynamically stable (under selection) subpopulations evenly spaced along the front. We show that this behavior can occur, but that it is highly unlikely. Rather, it is much more likely that a steady-state will be reached in which stable niches are maintained, but at inter-niche distances much less than the specified niche radius, with several times more niches than previously predicted, and with non-uniform sub-population sizes. These results might have implications for EMO population sizing, and perhaps even for EMO algorithm design itself.