On Meme Self-Adaptation in Spatially-Structured Multimemetic Algorithms

NMA 2014Multimemetic algorithms (MMAs) are memetic algorithms that explicitly exploit the evolution of memes, i.e., non-genetic expressions of problem-solving strategies. We consider a class of MMAs in which these memes are rewriting rules whose length can be fixed during the run of the algorithm or self-adapt during the search process. We analyze this self-adaptation in the context of spatially-structured MMAs, namely MMAs in which the population is endowed with a certain topology to which interactions (from the point of view of selection and variation operators) are constrained. For the problems considered, it is shown that panmictic (i.e., non-structured) MMAs are more sensitive to this self-adaptation, and that using variable-length memes seems to be a robust strategy throughout different population structures.This work is partially supported by MICINN project ANYSELF (TIN2011-28627-C04-01), by Junta de Andaluía project DNEMESIS (P10-TIC-6083) and by Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech

Sensitivity Analysis of Checkpointing Strategies for Multimemetic Algorithms on Unstable Complex Networks

The use of volatile decentralized computational platforms such as, e.g., peer-to-peer networks, is becoming an increasingly popular option to gain access to vast computing resources. Making an effective use of these resources requires algorithms adapted to such a changing environment, being resilient to resource volatility. We consider the use of a variant of evolutionary algorithms endowed with a classical fault-tolerance technique, namely the creation of checkpoints in a safe external storage. We analyze the sensitivity of this approach on different kind of networks (scale-free and small-world) and under different volatility scenarios. We observe that while this strategy is robust under low volatility conditions, in cases of severe volatility performance degrades sharply unless a high checkpoint frequency is used. This suggest that other fault-tolerance strategies are required in these situations.Universidad de Málaga, Campus de Excelencia Internacional Andalucía Tech. This work is partially supported by the MINECO project EphemeCH (TIN2014-56494-C4-1-P), by the Junta de Andalucía project DNEMESIS (P10-TIC-6083

Algoritmos Meméticos con Propiedades Self-* para la Optimización de Problemas Complejos

Las propiedades self-* de un sistema son aquellas que le permiten controlar de forma autónoma diferentes aspectos de su funcionamiento. En esta tesis doctoral se estudia el diseño y desarrollo de algoritmos meméticos con propiedades self-* a partir de una clase de algoritmo multimemético (MMA) con estructura espacial. En este MMA la población se dispone conforme a una cierta topología que permite restringir las interacciones entre los individuos, y en él los memes se definen como reglas de reescritura. Estos memes están sujetos a un proceso evolutivo propio similar al de la evolución genética mediante el cual pueden variar su estructura y complejidad, auto-optimizando de esta forma el proceso de búsqueda local. En este contexto se estudia la propagación y difusión de los memes a través de la población, proceso en el que la calidad de estos últimos solo se percibe indirectamente por el efecto que producen sobre los genotipos. Considerando el modelo teórico anterior como sustrato se incorporan características adicionales al MMA. Por un lado se crean algoritmos híbridos con el uso de modelos probabilísticos para la generación de la descendencia utilizando algoritmos de estimación de distribuciones (EDAs) y por otro, se consideran MMAs basados en islas. Este último modelo distribuido es objeto de un estudio más detallado, analizándose cómo afecta a su funcionamiento la utilización de diferentes políticas de migración de individuos entre nodos y el impacto que sobre el rendimiento de los mismos tiene la inestabilidad del entorno donde se ejecutan. Para ello se diseñan mecanismos de tolerancia a fallos y se estudia la utilización de redes complejas como topología de interconexión de los nodos. Asimismo, se proporciona al algoritmo la capacidad de escalabilidad automática mediante técnicas de auto-equilibrado de la carga, de forma tal que el propio MMA sea capaz, por sí mismo y sin necesidad de recurrir a un control central, de auto-adaptarse a la volatilidad del entorno. Finalmente se incorporan procedimientos de auto-reparación para compensar el deterioro producido por dicha inestabilidad: (i) auto-muestreo a través de un modelo probabilístico dinámico sobre las poblaciones de los nodos y (ii) auto-adaptación de la topología de interconexión a medida que diferentes nodos de cómputo entran o abandonan el sistema. Los experimentos realizados permiten concluir que la auto-adaptación de los memes contribuye a mejorar el rendimiento del MMA, así como que los modelos híbridos que utilizan EDAs proporcionan resultados notables, preferentemente los basados en distribuciones bivariadas. Con respecto al modelo de islas, las políticas de migración relativas a la selección de los migrantes o la estrategia de reemplazo de estos en la isla receptora son determinantes. Asimismo, las estrategias de gestión de fallos basadas en puntos de restauración mitigan la degradación del rendimiento conforme la red se vuelve más volátil, si bien conllevan sobrecargas computacionales. Como alternativa, la incorporación de propiedades self-* tales como el auto-equilibrado de la carga, el auto-muestreo probabilístico o la auto-adaptación de la topología de la red, tiene un impacto claramente positivo en el sistema, limitando su degradación en escenarios altamente inestables

A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or behaviour are. This fact has placed the users’ tracking process among the most difficult and determining issues in cellular network design. In this paper, we present an adaptive bi-phased evolutionary algorithm based on the takeover time to solve this problem. The proposal is thoroughly assessed by tackling twenty-five real-world instances of different sizes. Twenty-eight of the state-of-the-art techniques devised to address the users’ mobility problem have been taken as the comparison basis, and several statistical tests have been also conducted. Experiments have demonstrated that our solver outperforms most of the top-ranked algorithms.This research is partially funded by the Universidad de Málaga, Consejería de Economía y Conocimiento de la Junta de Andalucía and FEDER under grant number UMA18-FEDERJA-003 (PRECOG); MCIN/AEI/10.13039/501100011033 under grant number PID 2020-116727RB-I00 (HUmove) and under TAILOR ICT-48 Network (No952215) funded by EU Horizon 2020 research and innovation programme. Funding for open access charge is supported by the Universidad de Málaga/CBUA. The views expressed are purely those of the writer and may not in any circumstances be regarded as stating an official position of the European Commission. We also acknowledge that some instances studied in our work were previously inspired from the CRAWDAD dataset spitz/cellular. The authors would like also to address special thanks to Mrs Malika Belaifa and Mrs Zeineb Dahi for their help in creating the realistic problem benchmarks

Music in Evolution and Evolution in Music

Music in Evolution and Evolution in Music by Steven Jan is a comprehensive account of the relationships between evolutionary theory and music. Examining the ‘evolutionary algorithm’ that drives biological and musical-cultural evolution, the book provides a distinctive commentary on how musicality and music can shed light on our understanding of Darwin’s famous theory, and vice-versa. Comprised of seven chapters, with several musical examples, figures and definitions of terms, this original and accessible book is a valuable resource for anyone interested in the relationships between music and evolutionary thought. Jan guides the reader through key evolutionary ideas and the development of human musicality, before exploring cultural evolution, evolutionary ideas in musical scholarship, animal vocalisations, music generated through technology, and the nature of consciousness as an evolutionary phenomenon. A unique examination of how evolutionary thought intersects with music, Music in Evolution and Evolution in Music is essential to our understanding of how and why music arose in our species and why it is such a significant presence in our lives

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms