Abstract
An entropy-based metric is presented to assess the diversity of solutions in a multi-objective optimization technique. This metric quantifies the 'goodness' of a solution set in terms of its distribution quality over the Pareto-optimal frontier. As a demonstration via a three-objective test example, the entropy metric is used as a means of comparing two multi-objective genetic algorithms.
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