research-article

Neuroevolution of mobile ad hoc networks

Authors:

David B. Knoester,

Heather J. Goldsby,

Philip K. McKinleyAuthors Info & Claims

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

Pages 603 - 610

https://doi.org/10.1145/1830483.1830594

Published: 07 July 2010 Publication History

Abstract

This paper describes a study of the evolution of distributed behavior, specifically the control of agents in a mobile ad hoc network, using neuroevolution. In neuroevolution, a population of artificial neural networks (ANNs) are subject to mutation and natural selection. For this study, we compare three different neuroevolutionary systems: a direct encoding, an indirect encoding, and an indirect encoding that supports heterogeneity. Multiple variations of each of these systems were tested on a problem where agents were able to coordinate their collective behavior. Specifically, movement of agents in a simulated physics environment affected which agents were able to communicate with each other. The results of experiments indicate that this is a challenging problem domain for neuroevolution, and although direct and indirect encodings tended to perform similarly in our tests, the strategies employed by indirect encodings tended to favor stable, cohesive groups, while the direct encoding versions appeared more stochastic in nature.

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D. B. Knoester, H. J. Goldsby, and P. K. McKinley, "Evolution of controllers for mobile ad hoc networks," Tech. Rep. MSU-CSE-10-9, Computer Science and Engineering, Michigan State University, East Lansing, Michigan, April 2010.

Cited By

D’Ambrosio DGauci JStanley K(2014)HyperNEAT: The First Five YearsGrowing Adaptive Machines10.1007/978-3-642-55337-0_5(159-185)Online publication date: 5-Jun-2014
https://doi.org/10.1007/978-3-642-55337-0_5
Lehman JRisi SD’Ambrosio DO Stanley K(2013)Encouraging reactivity to create robust machinesAdaptive Behavior10.1177/105971231348739021:6(484-500)Online publication date: 20-Aug-2013
https://doi.org/10.1177/1059712313487390
Knoester DGoldsby HMcKinley P(2013)Genetic Variation and the Evolution of Consensus in Digital OrganismsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2012.220172517:3(403-417)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1109/TEVC.2012.2201725
Show More Cited By

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Neuroevolution of mobile ad hoc networks

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Published In

cover image ACM Conferences

GECCO '10: Proceedings of the 12th annual conference on Genetic and evolutionary computation

July 2010

1520 pages

ISBN:9781450300728

DOI:10.1145/1830483

General Chair:
Martin Pelikan
University of Missouri, USA
,
Program Chair:
Jürgen Branke
University of Warwick, Coventry, UK

Copyright © 2010 ACM.

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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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

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Published: 07 July 2010

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GECCO '10

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SIGEVO

GECCO '10: Genetic and Evolutionary Computation Conference

July 7 - 11, 2010

Oregon, Portland, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

D’Ambrosio DGauci JStanley K(2014)HyperNEAT: The First Five YearsGrowing Adaptive Machines10.1007/978-3-642-55337-0_5(159-185)Online publication date: 5-Jun-2014
https://doi.org/10.1007/978-3-642-55337-0_5
Lehman JRisi SD’Ambrosio DO Stanley K(2013)Encouraging reactivity to create robust machinesAdaptive Behavior10.1177/105971231348739021:6(484-500)Online publication date: 20-Aug-2013
https://doi.org/10.1177/1059712313487390
Knoester DGoldsby HMcKinley P(2013)Genetic Variation and the Evolution of Consensus in Digital OrganismsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2012.220172517:3(403-417)Online publication date: 1-Jun-2013
https://dl.acm.org/doi/10.1109/TEVC.2012.2201725
D’Ambrosio DStanley K(2013)Scalable multiagent learning through indirect encoding of policy geometryEvolutionary Intelligence10.1007/s12065-012-0086-36:1(1-26)Online publication date: 18-Jan-2013
https://doi.org/10.1007/s12065-012-0086-3
Lowell JGrabkovsky SBirger K(2011)Comparison of NEAT and HyperNEAT Performance on a Strategic Decision-Making ProblemProceedings of the 2011 Fifth International Conference on Genetic and Evolutionary Computing10.1109/ICGEC.2011.33(102-105)Online publication date: 29-Aug-2011
https://dl.acm.org/doi/10.1109/ICGEC.2011.33

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