The Naive MIDEA: A Baseline Multi-objective EA

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.

[1]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[2]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[3]  Thomas Bäck,et al.  Parallel Problem Solving from Nature — PPSN V , 1998, Lecture Notes in Computer Science.

[4]  Conor Ryan,et al.  Polygenic Inheritance - A Haploid Scheme that Can Outperform Diploidy , 1998, PPSN.

[5]  David E. Goldberg,et al.  The compact genetic algorithm , 1999, IEEE Trans. Evol. Comput..

[6]  James Kennedy,et al.  Proceedings of the 1998 IEEE International Conference on Evolutionary Computation [Book Review] , 1999, IEEE Transactions on Evolutionary Computation.

[7]  Gary B. Lamont,et al.  Multiobjective evolutionary algorithms: classifications, analyses, and new innovations , 1999 .

[8]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[9]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[10]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[11]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

[12]  D. Corne,et al.  On Metrics for Comparing Non Dominated Sets , 2001 .

[13]  Kalyanmoy Deb,et al.  Constrained Test Problems for Multi-objective Evolutionary Optimization , 2001, EMO.

[14]  Dirk Thierens,et al.  Multi-objective mixture-based iterated density estimation evolutionary algorithms , 2001 .

[15]  Marco Laumanns,et al.  Why Quality Assessment Of Multiobjective Optimizers Is Difficult , 2002, GECCO.

[16]  Dirk Thierens,et al.  Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms , 2002, Int. J. Approx. Reason..

[17]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[18]  Joshua D. Knowles,et al.  On metrics for comparing nondominated sets , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[19]  Dirk Thierens,et al.  The balance between proximity and diversity in multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..