Deb, K. ; Jain, S. (2002) Running performance metrics for evolutionary multi-objective optimizations Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), (Singapore) . pp. 13-20.
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Abstract
It is now well established that more than one performance metrics are necessary for evaluating a multi-objective evolutionary algorithm (MOEA). Although there exist a number of performance metrics in the MOEA literature, most of them are applied to the final non-dominated set obtained by an MOEA to evaluate its performance. In this paper, we suggest a couple of running metrics-one for measuring the convergence to a reference set and other for measuring the diversity in population members at every generation of an MOEA run. Either using a known Pareto-optimal front or an agglomeration of generation-wise populations, the suggested metrics reveal important insights and interesting dynamics of the working of an MOEA or help provide a comparative evaluation of two or more MOEAs.
| Item Type: | Article |
|---|---|
| Source: | Copyright of this article belongs to Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning (SEAL'02), (Singapore). |
| ID Code: | 81046 |
| Deposited On: | 03 Feb 2012 11:42 |
| Last Modified: | 03 Feb 2012 11:42 |
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