[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/1102256.1102339acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
Article

Why simulating evolutionary processes is just as interesting as applying them

Published: 25 June 2005 Publication History

Abstract

Evolutionary algorithms are very efficient tools to find a near-optimum solution in many cases. Until now they have been mostly used to find results but in this article we argue that evolutionary algorithms can also be used to simulate the evolution of complex systems. We model complex systems as networks in which agents are connected by edges if they interact with each other. It is known that many networks of this kind exhibit stable properties despite the dynamic processes they are subject to. We show here how evolutionary processes on complex systems can be modeled with a new kind of evolutionary algorithm which we have presented in [8]. We will show that some evolutionary processes within this framework yield networks with stable properties in reasonable time. An understanding of what kind of evolutionary processes will produce what kind of network properties in what time is vital to transfer evolutionary processes to technical ad-hoc networks in order to improve their flexibility and stability in quickly changing environments.

References

[1]
L. Adamic. The small world web. In Proceedings of ECDL'99 - Lecture Notes on Computer Science, pages 443--452, 1999.]]
[2]
L. Adamic and E. Adar. Friends and neighbors on the web, 2001.]]
[3]
R. Albert, H. Jeong, and A.-L. Barabási. Error and attack tolerance of complex networks. Nature, 406:378--382, 2000.]]
[4]
K. Börner, J. T. Maru, and R. L. Goldstone. The simultaneous evolution of author and paper networks. PNAS, 101, 2004.]]
[5]
G. E. P Box. Evolutionary operation: a method for increasing industrial productivity. Appl. Stat., 6(2):81--101, 1957.]]
[6]
M. Gleich. Web of Life - Die Kunst vernetzt zu leben. Hoffman und Campe, 1st edition, 2002.]]
[7]
J. H. Holland. Adaptation in natural and artificial systems. Ann Arbor: The University of Michigan Press, 1975.]]
[8]
K. A. Lehmann and M. Kaufmann. Evolutionary algorithms for the self-organized evolution of networks. In Proc. of GECCO 2005 (accepted), 2005.]]
[9]
C. Moore and M. E. J. Newman. Epidemics and percolation in small-world networks. Phys. Rev. E, 61:5678--5682, 2000.]]
[10]
I. Rechenberg. Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzoog, 1973.]]
[11]
D. J. Watts and S. Strogatz. Collective dynamics of small world networks. Nature, 393:440--442, 1998.]]

Cited By

View all
  • (2008)Optimizing self-organizing overlay network using evolutionary approachNeural Computing and Applications10.1007/s00521-007-0122-x17:2(129-138)Online publication date: 20-Feb-2008
  • (2005)Evolutionary algorithms for the self-organized evolution of networksProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068105(563-570)Online publication date: 25-Jun-2005

Recommendations

Reviews

Manish K Gupta

Biology has given us several new computing paradigms, and it has also provided us with many elegant solutions to difficult computing and communications problems. One such field is evolutionary computing, which is based on the principles given by Charles Darwin. In particular, evolutionary algorithms (EAs) have been used to find optimal solutions to many optimization problems. This has given us many diverse interdisciplinary applications of EAs in engineering and science. Complex systems are one such application. Complex systems are interactive selfish agents, investing in relations with each other for a personal gain. Each has access to limited resources such as money, time, and energy. In this paper, the author discusses the evolution of such complex systems as networks where nodes represent agents and the edges between them represent relations. He illustrates his point with two examples of social networks and power-law networks. Finally, in a formal framework, he provides evolutionary principles for the evaluation of complex systems of networks. In this framework, he illustrates the procedure for a connected tree-that is, a graph without cycles-and shows that this yields networks with stable properties in reasonable time. Online Computing Reviews Service

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '05: Proceedings of the 7th annual workshop on Genetic and evolutionary computation
June 2005
431 pages
ISBN:9781450378000
DOI:10.1145/1102256
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2005

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. evolution of networks
  2. evolutionary algorithms
  3. self-organization of complex systems

Qualifiers

  • Article

Conference

GECCO05
Sponsor:

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2008)Optimizing self-organizing overlay network using evolutionary approachNeural Computing and Applications10.1007/s00521-007-0122-x17:2(129-138)Online publication date: 20-Feb-2008
  • (2005)Evolutionary algorithms for the self-organized evolution of networksProceedings of the 7th annual conference on Genetic and evolutionary computation10.1145/1068009.1068105(563-570)Online publication date: 25-Jun-2005

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media