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Entropic divergence for population based optimization algorithms

Cutello, V and Nicosia, G and Pavone, M and Stracquadanio, G (2010) Entropic divergence for population based optimization algorithms. In: UNSPECIFIED, ? - ?.

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Abstract

The concept of information gain has been adopted as tool to study the effectiveness of population-based optimizers; using this approach, it is possible to infer convergence properties for population-based optimizers. The experimental results have shown characteristic phase transition between exploration and exploitation phase during the evolutionary process and, moreover, the evidence that gain maximization offers a robust theoretical framework to study the convergence of stochastic optimizers. © 2010 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Giovanni Stracquadanio
Date Deposited: 13 Feb 2017 14:09
Last Modified: 17 Aug 2017 17:20
URI: http://repository.essex.ac.uk/id/eprint/18711

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