Evolutionary Diversity Optimization Using Multi-Objective Indicators
This work addresses the need for effective diversity optimization in evolutionary algorithms, though it appears incremental by applying existing multi-objective indicators to a known bottleneck.
The paper tackled the problem of generating diverse sets of high-quality solutions by bridging evolutionary diversity optimization with multi-objective optimization, showing that using hypervolume and inverted generational distance indicators provides excellent results in terms of visualization and diversity metrics for TSP instances and images.
Evolutionary diversity optimization aims to compute a diverse set of solutions where all solutions meet a given quality criterion. With this paper, we bridge the areas of evolutionary diversity optimization and evolutionary multi-objective optimization. We show how popular indicators frequently used in the area of multi-objective optimization can be used for evolutionary diversity optimization. Our experimental investigations for evolving diverse sets of TSP instances and images according to various features show that two of the most prominent multi-objective indicators, namely the hypervolume indicator and the inverted generational distance, provide excellent results in terms of visualization and various diversity indicators.