Fast Global Optimization of Difficult Lennard-Jones Clusters
Computational Optimization and Applications
A Population-based Approach for Hard Global Optimization Problems based on Dissimilarity Measures
Mathematical Programming: Series A and B
Global Optimization of Morse Clusters by Potential Energy Transformations
INFORMS Journal on Computing
A Self-Adaptive Evolutionary Algorithm for Cluster Geometry Optimization
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Global optimization of binary Lennard-Jones clusters
Optimization Methods & Software - GLOBAL OPTIMIZATION
Learning individual mating preferences
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving fitness functions for mating selection
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
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Sexual Selection through Mate Choice has, over the past few decades, attracted the attention of researchers from various fields. They have gathered numerous supporting evidence, establishing Mate Choice as a major driving force of evolution, capable of shaping complex traits and behaviours. Despite its wide acceptance and relevance across various research fields, the impact of Mate Choice in Evolutionary Computation is still far from understood, both regarding performance and behaviour. In this study we describe a nature-inspired self-adaptive mate choice model, relying on a Genetic Programming representation tailored for the optimization of Morse clusters, a relevant and widely accepted problem for benchmarking new algorithms, which provides a set of hard test instances. The model is coupled with a state-of-the-art hybrid steady-state approach and both its performance and behaviour are assessed with a particular interest on the replacement strategy's acceptance rate and diversity handling.