Noise Handling in Evolutionary Multi-Objective Optimization

In addition to the need to satisfy several competing objectives, many real-world applications are also characterized by noise. In this paper, three noise-handling features, an experiential learning directed perturbation (ELDP) operator, a gene adaptation selection strategy (GASS) and a possibilistic archiving model are proposed. The ELDP adapts the magnitude and direction of variation according to past experiences for fast convergence while the GASS improves the evolutionary search in escaping from premature convergence in both noiseless and noisy environments. The possibilistic archiving model is based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multi-objective evolutionary algorithms in noisy environments as well as the robustness and effectiveness of the proposed features are examined based upon three benchmark problems characterized by different difficulties.

[1]  Hussein A. Abbass,et al.  Fitness inheritance for noisy evolutionary multi-objective optimization , 2005, GECCO '05.

[2]  David W. Corne,et al.  Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy , 2000, Evolutionary Computation.

[3]  C. Fonseca,et al.  GENETIC ALGORITHMS FOR MULTI-OBJECTIVE OPTIMIZATION: FORMULATION, DISCUSSION, AND GENERALIZATION , 1993 .

[4]  L. Darrell Whitley,et al.  Searching in the Presence of Noise , 1996, PPSN.

[5]  Peter J. Fleming,et al.  Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization , 1993, ICGA.

[6]  H. Beyer.  Evolutionary algorithms in noisy environments : theoretical issues and guidelines for practice , 2000 .

[7]  Marco Laumanns,et al.  SPEA2: Improving the strength pareto evolutionary algorithm , 2001 .

[8]  Hajime Kita,et al.  Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation , 2000, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[9]  Günter Rudolph,et al.  A partial order approach to noisy fitness functions , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Sandor Markon,et al.  Threshold selection, hypothesis tests, and DOE methods , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[11]  Jürgen Branke,et al.  Efficient fitness estimation in noisy environments , 2001 .

[12]  Evan J. Hughes,et al.  Evolutionary Multi-objective Ranking with Uncertainty and Noise , 2001, EMO.

[13]  Magnus Rattray,et al.  Noisy Fitness Evaluation in Genetic Algorithms and the Dynamics of Learning , 1996, FOGA.

[14]  Thomas Bäck,et al.  Evolution strategies applied to perturbed objective functions , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[15]  Tong Heng Lee,et al.  A Study on Distribution Preservation Mechanism in Evolutionary Multi-Objective Optimization , 2005, Artificial Intelligence Review.

[16]  P. Koumoutsakos,et al.  Multiobjective evolutionary algorithm for the optimization of noisy combustion processes , 2002 .

[17]  Hans-Georg Beyer,et al.  Local performance of the (1 + 1)-ES in a noisy environment , 2002, IEEE Trans. Evol. Comput..

[18]  Tong Heng Lee,et al.  Evolutionary algorithms with dynamic population size and local exploration for multiobjective optimization , 2001, IEEE Trans. Evol. Comput..

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

[20]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .