Abstract
Understanding the emergent collective behaviour (and the properties associated with it) of population-based algorithms is an important prerequisite for making technically sound choices of algorithms and also for designing new algorithms for specific applications. In this paper, we present an empirical approach to analyse and quantify the collective emergent behaviour of populations. In particular, our long term objective is to understand and characterise the notions of exploration and exploitation and to make it possible to characterise and compare algorithms based on such notions. The proposed approach uses self-organising maps as a tool to track the population dynamics and extract features that describe a population “functionality” and “structure”.
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Turkey, M., Poli, R. (2012). An Empirical Tool for Analysing the Collective Behaviour of Population-Based Algorithms. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_11
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DOI: https://doi.org/10.1007/978-3-642-29178-4_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29177-7
Online ISBN: 978-3-642-29178-4
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