Running performance Metrics for evolutionary multi-objective optimization (2002)
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BibTeX
@TECHREPORT{Deb02runningperformance,
author = {Kalyanmoy Deb and Sachin Jain},
title = {Running performance Metrics for evolutionary multi-objective optimization},
institution = {},
year = {2002}
}
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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.
