- author = "Antonio Leitao and Penousal Machado",
- title = "Self-adaptive mate choice for cluster geometry optimization",
- booktitle = "GECCO '13: Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference",
- year = "2013",
- editor = "Christian Blum and Enrique Alba and Anne Auger and Jaume Bacardit and Josh Bongard and Juergen Branke and Nicolas Bredeche and Dimo Brockhoff and Francisco Chicano and Alan Dorin and Rene Doursat and Aniko Ekart and Tobias Friedrich and Mario Giacobini and Mark Harman and Hitoshi Iba and Christian Igel and Thomas Jansen and Tim Kovacs and Taras Kowaliw and Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and John McCall and Alberto Moraglio and Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and Gustavo Olague and Yew-Soon Ong and Michael E. Palmer and Gisele Lobo Pappa and Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and Daniel Tauritz and Leonardo Vanneschi",
- isbn13 = "978-1-4503-1963-8",
- pages = "957--964",
- keywords = "genetic algorithms, genetic programming",
- month = "6-10 " # jul,
- organisation = "SIGEVO",
- address = "Amsterdam, The Netherlands",
-
DOI = "
doi:10.1145/2463372.2463494",
- publisher = "ACM",
- publisher_address = "New York, NY, USA",
-
abstract = "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 optimisation 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.",
- notes = "Also known as \cite{2463494} GECCO-2013 A joint meeting of the twenty second international conference on genetic algorithms (ICGA-2013) and the eighteenth annual genetic programming conference (GP-2013)",
