Abstract
At the beginning of this year one of the authors read “A brave new world”, a novel by Aldous Huxley. This book describes a dystopia, which anticipates the development of world-scale breeding technology, and how this technology creates the optimal human race. Taking into account that when talking about genetic algorithms our goal is to achieve the optimum solution of a problem, and this book kind of describes the process for making the “perfect human”, or rather the “perfect human population”, we will try to work on this parallelism in this paper, trying to find what is the key to the evolution processes described in the book. The goal is to develop a genetic algorithm based on the fecundation process of the book and compare it to other algorithms to see how it behaves, by investigating how the division in castes affects the diversity in the poblation. In this paper we describe the implementation of such algorithm in the programming language Julia, and how design and implementation decisions impact algorithmic and runtime performance.
This paper has been supported in part by projects DeepBio (TIN2017–85727–C4–2–P) and DemocratAI PID2020-115570GB-C22.
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Merelo, C., Merelo, J.J., García-Valdez, M. (2022). A Brave New Algorithm to Maintain the Exploration/Exploitation Balance. In: Castillo, O., Melin, P. (eds) New Perspectives on Hybrid Intelligent System Design based on Fuzzy Logic, Neural Networks and Metaheuristics. Studies in Computational Intelligence, vol 1050. Springer, Cham. https://doi.org/10.1007/978-3-031-08266-5_20
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