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Implicit Multi-Objective Coevolutionary Algorithm

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Title: Implicit Multi-Objective Coevolutionary Algorithm
Author: Akinola, Adefunke
Department: School of Computer Science
Program: Computer Science
Advisor: Wineberg, Mark
Abstract: Coevolutionary algorithms are powerful tools for solving increasingly complex problems by explicitly evolving solutions in the form of interacting co-adapted subcomponents. Its fitness is subjective – fitness of individuals relative to other individuals in the population(s) – which theoretically aids the convergence rate but unfortunately births the red-queen effect. Consequently, although elitism is easy to define within a generation, it becomes hard to define across generations. Furthermore, this effect can also cause forgetfulness to occur. We noticed that, while coevolutionary systems typically have only a single objective for evaluation, there is a subtle multi-objective aspect to evaluation that we feel necessitates a method to regulate the pairings of individuals both within and between generations. This research investigates this implicit multi-objective nature of coevolutionary systems, which, as it turns out, makes it possible to manage elitism by addressing forgetfulness and managing the Red-Queen effect, thus providing robust solutions.
URI: http://hdl.handle.net/10214/17493
Date: 2019-09
Rights: Attribution-ShareAlike 4.0 International
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Attribution-ShareAlike 4.0 International Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International