Novel Genotypic Diversity Metrics for Real-Coded Optimization on Multi-Modal Problems

This paper presents two new Genotypic Diversity Metrics (GDMs) for estimating the diversity of populations in real-coded, multi-modal optimization problems. The two techniques introduced are the Minimum Individual Distance (DMID) and Radius Diversity (DRaD). The design of these two metrics focuses on the relative position of the solutions in the population. This choice addresses problems observed in traditional diversity measures regarding their behavior as the number of optima increases in a multi-modal problem. The proposed metrics are evaluated on four different experiments, which investigate the metrics' theoretical and practical properties. Experimental results reveal several abnormal behaviors of the Pairwise Distance Metric (Dpw), while showing that the proposed metrics perform in a more stable and intuitive way as the number of optima in the problem increases. These results recommend the metrics' use for developing and evaluating optimization meta-heuristics