Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
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Multi-objective evolutionary algorithms (MOEAs) have been the mainstream to solve multi-objectives optimization problems In this paper we add the static Bayesian game strategy into MOGA and propose a novel multi-objective genetic algorithm(SBG-MOGA) Conventional MOGAs use non-dominated sorting methods to push the population to move toward the real Pareto front This approach has a good performance at earlier stages of the evolution, however it becomes hypodynamic at the later stages In SBG-MOGA the objectives to be optimized are similar to players in a static Bayesian game A player is a rational person who has his own strategy space A player selects a strategy and takes an action to realize his strategy in order to achieve the maximal income for the objective he works on The game strategy will generate a tensile force over the population and this will obtain a better multi-objective optimization performance Moreover, the algorithm is verified by several simulation experiments and its performance is tested by different benchmark functions.