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Neural Nets WIRN Vietri-01 pp 144–150Cite as

Payoff-monotonic Game Dynamics for the Maximum Clique Problem

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Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

Abstract

Replicator equations, which arise in evolutionary game theory to model the evolution of animal behavior, have recently been applied with significant success to combinatorial optimization problems such as the maximum clique problem. This paper substantially expands on previous work along these lines, by proposing payoff-monotonic dynamics, a wide family of game dynamics of which replicator equations are just a special instance. Experiments show that this class contains dynamics which are considerably faster than and as accurate as replicator equations.

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References

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Author information

Authors and Affiliations

  1. Dipartimento di Informatica, Università Ca’ Foscari di Venezia, Via Torino 155, 30173, Mestre (Venezia), Italy

    Marcello Pelillo & Claudio Rossi

Authors
  1. Marcello Pelillo
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  2. Claudio Rossi
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Editor information

Editors and Affiliations

  1. DMI, Università di Salerno, 84081, Baronissi (SA), Italy

    Roberto Tagliaferri (Associate Professor of Computer Science and Neural Nets DMI) (Associate Professor of Computer Science and Neural Nets DMI)

  2. Dipartimento di Scienze Fisiche „E.R. Caianiello“, Università di Salerno, 84081, Baronissi (SA), Italy

    Maria Marinaro (Full Professor in Theoretical Physics) (Full Professor in Theoretical Physics)

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© 2002 Springer-Verlag London Limited

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Pelillo, M., Rossi, C. (2002). Payoff-monotonic Game Dynamics for the Maximum Clique Problem. In: Tagliaferri, R., Marinaro, M. (eds) Neural Nets WIRN Vietri-01. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0219-9_13

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  • DOI: https://doi.org/10.1007/978-1-4471-0219-9_13

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-505-2

  • Online ISBN: 978-1-4471-0219-9

  • eBook Packages: Springer Book Archive

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