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
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Why Quality Assessment Of Multiobjective Optimizers Is Difficult
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Multiobjective evolutionary algorithms: classifications, analyses, and new innovations
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Exploiting gradient information in numerical multi--objective evolutionary optimization
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning, anticipation and time-deception in evolutionary online dynamic optimization
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Combining gradient techniques for numerical multi-objective evolutionary optimization
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
The RM-MEDA based on elitist strategy
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Advancing model–building for many–objective optimization estimation of distribution algorithms
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Annals of Mathematics and Artificial Intelligence
| Hi-index | 0.00 |
Estimation of distribution algorithms have been shown to perform well on a wide variety of single–objective optimization problems. Here, we look at a simple – yet effective – extension of this paradigm for multi–objective optimization, called the naive ${\mathbb M}$ID${\mathbb E}$A. The probabilistic model in this specific algorithm is a mixture distribution, and each component in the mixture is a univariate factorization. Mixture distributions allow for wide–spread exploration of the Pareto front thus aiding the important preservation of diversity in multi–objective optimization. Due to its simplicity, speed, and effectiveness the naive ${\mathbb M}$ID${\mathbb E}$A can well serve as a baseline algorithm for multi–objective evolutionary algorithms.