Advertisement

A New Parallel Memetic Algorithm to Knowledge Discovery in Data Mining

  • Dahmri OualidEmail author
  • Ahmed Riadh Baba-Ali
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10103)

Abstract

This paper presents a new parallel memetic algorithm (PMA) for solving the problem of classification in the process of Data Mining. We focus our interest on accelerating the PMA. In most parallel algorithms, the tasks performed by different processors need access to shared data, this creates a need for communication, which in turn slows the performance of the PMA. In this work, we will present the design of our PMA, In which we will use a new replacement approach, which is a hybrid approach that uses both Lamarckian and Baldwinian approaches at the same time, to reduce the quantity of informations exchanged between processors and consequently to improve the speedup of the PMA. An extensive experimental study performed on the UCI Benchmarks proves the efficiency of our PMA. Also, we present the speedup analysis of the PMA.

Keywords

Parallel memetic algorithm Classification Extraction of rules Lamarckian approach Baldwinian approach Hybridization 

References

  1. 1.
    Cios, K.J., Pedryecz, W., Swinniarsky, R.W., Kurgan, A., et al.: Data Mining: A Knowledge Discovery Approach. Editions Springer Science (2007)Google Scholar
  2. 2.
    Jain, A.K., Dubes, R.C.: Algorithms for clustering data. Editions Prentice Hall Advanced Reference Series. Prentice Hall, New Jersey (1988)Google Scholar
  3. 3.
    Dréo, J., Pétrowski, A., Siarry, P., Taillard, E.: Métaheuristiques pour l’optimisation difficile. Eyrolles (2005)Google Scholar
  4. 4.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: overview and conceptual comparison. ACM Comput. Surv. 35(3), 268–308 (2003)Google Scholar
  5. 5.
    Hao, J.-K., Galinier, P., Habib, M.: Métaheuristiques pour l’optimisation combinatoire et l’affectation sous contraintes. Revue d’intelligence artificielle (1999)Google Scholar
  6. 6.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Massachusetts (1989)zbMATHGoogle Scholar
  7. 7.
    Glover, F.: Tabu search - Part I. ORSA J. Comput. 1(3), 190–206 (1989)Google Scholar
  8. 8.
    Crainic, T.G., Toulouse, M., et al.: Parallel Metaheuristics. In: Crainic, T.G.¸ Laporte, G. (eds.) Fleet Management and Logistics, pp. 205–251. Kluwer Academic, Norwell (1998)Google Scholar
  9. 9.
    Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs parallèles, réseaux et systèmes répartis 10(2), 141–171 (1998)Google Scholar
  10. 10.
    Bachelet, V., Hafidi, Z., Preux, P., Talbi, E.-G.: Vers la coopération des métaheuristiques. Calculateurs parallèles, réseaux et systèmes répartis 10(2) (1998)Google Scholar
  11. 11.
    Alba, E., Luque, G.: IV leasuring the performance of parallel metaheuristics. In: Parallel Metaheuristics: A new Class of Algorithms. Wiley-Interscience (2005)Google Scholar
  12. 12.
    Barr, R., Hickman, B.: Reporting Computational Experiments with ParaUel Algorithms: Issues, Measures, and Experts’ Opinions. Dept. of Computer Science and Engineering, Southern Tvlethodist University (1992)Google Scholar
  13. 13.
    Malony, A.: Tools for Parallel Computing: A Performance Evaluation Perspective, ch. VII, p. 342. Springer (2000)Google Scholar
  14. 14.
    Bacardit, J.: Pittsburgh Genetic-Based Machine Learning in the Data Mining era: Representations, Generalization, and Run-time. Ph.d. Thesis, Universitat Ramon LIul, Spain (2004)Google Scholar
  15. 15.
    Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufman Publishers, San Mateo (2003)Google Scholar
  16. 16.
    Tan, K.C., Yu, Q., Ang, J.H.: A dual-objective evolutionary algorithm for rules extraction in data mining. Comput. Optim. Appl. 34, 273–294 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998)Google Scholar
  18. 18.
    Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Caltech concurrent computation program, C3P Report 826 (1989)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer Science Department, FEI, USTHBBab EzzouarAlgeria
  2. 2.Research Laboratory LRPE, FEI, USTHBBab EzzouarAlgeria

Personalised recommendations