Clustering in evolutionary algorithms to efficiently compute simultaneously local and global minima

In this paper a new clustering operator for evolutionary algorithms is proposed. The operator incorporates the unsupervised k-windows clustering algorithm, utilizing already computed pieces of information regarding the search space in an attempt to discover regions containing groups of individuals located close to different minimizers. Consequently, the search is confined inside these regions and a large number of global and local minima of the objective function can be efficiently computed. Extensive experiments shown that the proposed approach is effective and reliable, and greatly accelerates the convergence speed of the considered algorithms.

[1]  Franco P. Preparata,et al.  Sequencing-by-hybridization revisited: the analog-spectrum proposal , 2004, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  Vassilis P. Plagianakos,et al.  Neural network training with constrained integer weights , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[3]  Graham K. Rand,et al.  Quantitative Applications in the Social Sciences , 1983 .

[4]  Hans-Paul Schwefel,et al.  Evolution and optimum seeking , 1995, Sixth-generation computer technology series.

[5]  Vassilis P. Plagianakos,et al.  Training neural networks with threshold activation functions and constrained integer weights , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[6]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[7]  Dimitris K. Tasoulis,et al.  Parallelizing the Unsupervised k-Windows Clustering Algorithm , 2003, PPAM.

[8]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[9]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[10]  Kuldip K. Paliwal,et al.  Fast K-dimensional tree algorithms for nearest neighbor search with application to vector quantization encoding , 1992, IEEE Trans. Signal Process..

[11]  Vassilis P. Plagianakos,et al.  Parallel evolutionary training algorithms for “hardware-friendly” neural networks , 2002, Natural Computing.

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Dimitris K. Tasoulis,et al.  Parallel Unsupervised k-Windows: An Efficient Parallel Clustering Algorithm , 2003, PaCT.

[14]  Rainer Storn,et al.  System design by constraint adaptation and differential evolution , 1999, IEEE Trans. Evol. Comput..

[15]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[16]  Robert G. Reynolds,et al.  Evolutionary computation: Towards a new philosophy of machine intelligence , 1997 .

[17]  Roger K. Blashfield,et al.  Cluster Analysis and Archaeological Classification , 1978, American Antiquity.

[18]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[19]  V. P. Plagianakos,et al.  TUMOR DETECTION IN COLONOSCOPY USING THE UNSUPERVISED k-WINDOWS CLUSTERING ALGORITHM AND NEURAL NETWORKS , 2004 .

[20]  Dimitris K. Tasoulis,et al.  Unsupervised Clustering of Bioinformatics Data , 2004 .

[21]  Dimitris K. Tasoulis,et al.  UNSUPERVISED CLUSTER ANALYSIS IN BIOINFORMATICS , 2004 .

[22]  Jouni Lampinen,et al.  A Trigonometric Mutation Operation to Differential Evolution , 2003, J. Glob. Optim..

[23]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[24]  A. A. Torn.  Cluster Analysis Using Seed Points and Density-Determined Hyperspheres as an Aid to Global Optimization , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  J. Suh,et al.  A cross-validation of the biphasic poroviscoelastic model of articular cartilage in unconfined compression, indentation, and confined compression. , 2001, Journal of biomechanics.

[26]  Rainer Storn,et al.  Differential evolution design of an IIR-filter , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[27]  Leen Stougie,et al.  Global optimization : a stochastic approach , 1980 .

[28]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[29]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[30]  P. Sopp.  Cluster analysis. , 1996, Veterinary immunology and immunopathology.

[31]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[32]  Rüdiger L. Urbanke,et al.  Design of capacity-approaching irregular low-density parity-check codes , 2001, IEEE Trans. Inf. Theory.

[33]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[34]  F. Frances Yao,et al.  Computational Geometry , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[35]  M. Aldenderfer,et al.  Cluster Analysis. Sage University Paper Series On Quantitative Applications in the Social Sciences 07-044 , 1984 .

[36]  Dimitris K. Tasoulis,et al.  Online Neural Network Training for Automatic Ischemia Episode Detection , 2004, ICAISC.

[37]  Paul H. Siegel,et al.  Performance analysis and code optimization of low density parity-check codes on Rayleigh fading channels , 2001, IEEE J. Sel. Areas Commun..

[38]  W. Pinebrook.  The evolution of strategy. , 1990, Case studies in health administration.

[39]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[40]  Dimitris K. Tasoulis,et al.  Financial forecasting through unsupervised clustering and evolutionary trained neural networks , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[41]  Joni-Kristian Kämäräinen,et al.  Differential Evolution Training Algorithm for Feed-Forward Neural Networks , 2003, Neural Processing Letters.

[42]  Dimitris K. Tasoulis,et al.  Unsupervised distributed clustering , 2004, Parallel and Distributed Computing and Networks.