Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Bayesian Optimization Algorithms for Multi-objective Optimization
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
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A new real-coded multi-objective estimation of distribution algorithm (RCMEDA) for optimization problems with continuous variables is developed. Decision tree is used for discretization to encode conditional dependencies among variables in RCMEDA, i.e. decision-tree-based probabilistic model is used. By building and sampling the probabilistic models, the algorithm reproduces the genetic information of the next generation. Incorporating this reproduction mechanism together with the ranking method and the truncated selection, RCMEDA can approximate the Pareto front. And polynomial mutation operator is used in order to enhance exploration and maintain diversities in the populations. Furthermore, RCMEDA adopts a procedure to eliminate a solution with smallest crowding distance at a time in the truncated selection, so that it can obtain a well spread solution set. The performance of the proposed algorithm is evaluated on four test problems and metrics from literature. Simulation results show that the proposed approach is competitive with NSGA-II and RCMEDA is a general and effective method for multi-objective optimization.