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Peer-to-peer evolutionary algorithms with adaptive autonomous selection

Published: 07 July 2007 Publication History

Abstract

In this paper we describe and evaluate a fully distributed P2P evolutionary algorithm (EA) with adaptive autonomous selection. Autonomous selection means that decisions regarding survival and reproduction are taken by the individuals themselves independently, without any central control.This allows for a fully distributed EA, where not only reproduction (crossover and mutation) but also selection is performed at local level. An unwanted consequence of adding and removing individuals in a non-synchronized manner is that the population size gets out of control too. This problem is resolved by addingan adaptation mechanism allowing individuals to regulate their own selection pressure. The key tothis is a gossiping algorithm that enables individuals to maintain estimates on the size andthe fitness of the population. The algorithm is experimentally evaluated on a test problem to show the viability of the idea and to gain insight into the run-time dynamics of such an algorithm. The results convincingly demonstrate the feasibility of a fully decentralized EA in which the population size can be kept stable.

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cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 July 2007

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Author Tags

  1. autonomous selection
  2. distributed EA
  3. gossiping
  4. newscast protocol
  5. parameter adaptation

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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