Enhancing distributed EAs by a proactive strategy

Authors
Salto, Carolina; Luna, Francisco; Alba, Enrique
Publication Year
2014
Language
English
Format
article
Status
Published version
Description
In this work we propose a new distributed evolutionary algorithm that uses a proactive strategy to adapt its migration policy and the mutation rate. The proactive decision is carried out locally in each subpopulation based on the entropy of that subpopulation. In that way, each subpopulation can change their own incoming flow of individuals by asking their neighbors for more frequent or less frequent migrations in order to maintain the genetic diversity at a desired level. Moreover, this proactive strategy is reinforced by adapting the mutation rate while the algorithm is searching for the problem solution. All these strategies avoid the subpopulations to get trapped into local minima. We conduct computational experiments on large instances of the NK landscape problem which have shown that our proactive approach outperforms traditional dEAs, particularly for not highly rugged landscapes, in which it does not only reaches the most accurate solutions, but it does the fastest.
Fil: Salto, Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de La Pampa; Argentina
Fil: Luna, Francisco. Universidad Carlos III de Madrid; España
Fil: Alba, Enrique. Universidad de Málaga; España
Subject
proactive behaviour
distributed EAs
migration period
Ciencias de la Computación
Ciencias de la Computación e Información
CIENCIAS NATURALES Y EXACTAS
Access level
Restricted access
License
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repository
CONICET Digital (CONICET)
Institution
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identifier
oai:ri.conicet.gov.ar:11336/36446