| Title: | Performance analysis of evolutionary multi-objective optimization algorithms in noisy environments |
| Abstract: | In this paper, we present performance comparisons between two popular elitism–based evolutionary multi-objective optimization algorithms - NSGA2 and SPEA2 in the presence of noise. Three test problems and six noise levels are employed in the research experiments. The results show that SPEA2 outperforms NSGA2 in the early generations. NSGA2, however, is superior during latter generations regardless of the level of noise presence in the problem. |
| Authors: | Lam T. Bui, Daryl Essam, Hussein A. Abbass, David Green |
| Affiliation: | Artificial Life and Adaptive Robotics Laboratory, School of ITEE, UNSW@ADFA and Faculty of Information Technology Monash University |
| Topics: | Artificial Life and Application, Computational Intelligence, Machine Learning Systems , Evolutionary Algorithms, Genetic Algorithms, Multiple-objective Optimisation, Soft Computing, |
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