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)


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.


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


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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

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