The EvoSpace Model for Pool-Based Evolutionary Algorithms

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• 作者：Mario García-Valdez ; Leonardo Trujillo ; Juan-J Merelo…
• 关键词：Pool ; based evolutionary algorithms ; Distributed evolutionary algorithms ; Heterogeneous computing platforms for bioinspired algorithms ; Parameter setting
• 刊名：Journal of Grid Computing
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：13
• 期：3
• 页码：329-349
• 全文大小：2,164 KB
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• 作者单位：Mario García-Valdez (1)
  Leonardo Trujillo (2)
  Juan-J Merelo (3)
  Francisco Fernández de Vega (4)
  Gustavo Olague (5)
  
  1. Instituto Tecnológico de Tijuana, Calzada Tecnológico S/N, Tijuana, BC, 22414, Mexico
  2. Departamento de Ingeniería Eléctrica y Electrónica, Posgrado en Ciencias de la Ingeniería, Instituto Tecnológico de Tijuana, Calzada Tecnológico S/N, Tijuana, BC, 22414, Mexico
  3. Departamento de Arquitectura y Tecnología de Computadores, Centro de Investigación en Tecnologías de la Información y las Comunicaciones, Universidad de Granada, Granada, Spain
  4. Grupo de Evolución Artificial, Universidad de Extremadura, Extremadura, Spain
  5. Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, BC, Mexico
  
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Management of Computing and Information Systems
  User Interfaces and Human Computer Interaction
  
• 出版者：Springer Netherlands
• ISSN：1572-9184

文摘

This work presents the EvoSpace model for the development of pool-based evolutionary algorithms (Pool-EA). Conceptually, the EvoSpace model is built around a central repository or population store, incorporating some of the principles of the tuple-space model and adding additional features to tackle some of the issues associated with Pool-EAs; such as, work redundancy, starvation of the population pool, unreliability of connected clients or workers, and a large parameter space. The model is intended as a platform to develop search algorithms that take an opportunistic approach to computing, allowing the exploitation of freely available services over the Internet or volunteer computing resources within a local network. A comprehensive analysis of the model at both the conceptual and implementation levels is provided, evaluating performance based on efficiency, optima found and speedup, while providing a comparison with a standard EA and an island-based model. The issues of lost connections and system parametrization are studied and validated experimentally with encouraging results, that suggest how EvoSpace can be used to develop and implement different Pool-EAs for search and optimization. Keywords Pool-based evolutionary algorithms Distributed evolutionary algorithms Heterogeneous computing platforms for bioinspired algorithms Parameter setting