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Studying self-balancing strategies in island-based multimemetic algorithms

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2016年 293卷 180-191页

作者： Nogueras, Rafael Cotta, Carlos Univ Malaga Dept Lenguajes & Ciencias Computac ETSI Informat E-29071 Malaga Spain

multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We aim to study their deployment on an unstable environment with complex topology and volatile resources. We analyze their behavior and performance on environments with different churn rates, and how they are affected by the use of self-balancing strategies aiming to compensate the loss of existing islands and react to the apparition of new ones. We investigate two such strategies, one based on quantitative balance (in which populations are resized dynamically to cope with node failure/recoveries) and another on qualitative balance (in which genetic/memetic information is actually exchanged to achieve balance). We evaluate these on scale-free network topologies and compare them to an unbalanced strategy that keeps island sizes constant. Experimentation firstly focuses on memetic takeover, carried out on an idealized selecto-Lamarckian model of MMAs (used as a surrogate of the latter) and indicating that the two balancing strategies exhibit complementary profiles in terms of diversity preservation. The results also indicate that the qualitative version is more robust to churn than both the unbalanced and the quantitatively balanced counterpart. This is subsequently confirmed with an empirical evaluation of full-fledged MMAs on a benchmark composed of four hard pseudo-Boolean problems. The qualitative version provides the best performance in global terms, significantly outperforming the remaining variants. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Memetic algorithms multimemetic algorithms Load balancing Self-adaptation Faulty environment

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Analyzing Meme Propagation in multimemetic algorithms: Initial Investigations

Analyzing Meme Propagation in Multimemetic Algorithms: Initi...

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Federated Conference on Computer Science and Information Systems

作者： Rafael Nogueras Carlos Cotta ETSI Informática Universidad de Málaga Campus de Teatinos 29071 Málaga Spain

ISBN: (纸本)9781467344715

multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grids topology. We show that the increased takeover time induced by the latter is essential to improve the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates.

关键词： MMAs Analyzing Meme Propagation multimemetic algorithms

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