An Adaptive Global-Local Memetic Algorithm to Discover Resources in P2P Networks

Authors:
Ferrante Neri;Niko Kotilainen;Mikko Vapa
Affiliations:
Department of Mathematical Information Technology, Agora, University of Jyväskylä, FI-40014, Finland and Dipartimento di Elettrotecnica ed Elettronica, Politecnico di Bari, Via E. Orabon ...;Department of Mathematical Information Technology, Agora, University of Jyväskylä, FI-40014, Finland;Department of Mathematical Information Technology, Agora, University of Jyväskylä, FI-40014, Finland
Venue:
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Year:
2009

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Abstract

This paper proposes a neural network based approach for solving the resource discovery problem in Peer to Peer (P2P) networks and an Adaptive Global Local Memetic Algorithm (AGLMA) for performing the training of the neural network. This training is very challenging due to the large number of weights and noise caused by the dynamic neural network testing. The AGLMA is a memetic algorithm consisting of an evolutionary framework which adaptively employs two local searchers having different exploration logic and pivot rules. Furthermore, the AGLMA makes an adaptive noise compensation by means of explicit averaging on the fitness values and a dynamic population sizing which aims to follow the necessity of the optimization process. The numerical results demonstrate that the proposed computational intelligence approach leads to an efficient resource discovery strategy and that the AGLMA outperforms two classical resource discovery strategies as well as a popular neural network training algorithm.