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An Efficient PSO-GA Based Back Propagation Learning-MLP (PSO-GA-BP-MLP) for Classification

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Computational Intelligence in Data Mining - Volume 1

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 31))

Abstract

In last few decades, Evolutionary computation and Swarm intelligence are two hot favorites for almost all types of researchers. Moreover, many contributions have been made in two directions: Genetic Algorithm (GA) and Particle Swarm optimization (PSO). But, some limitations in both the algorithms (complicated operator like crossover and mutation in GA and early convergence in PSO), are the major restricted boundaries for solving complex problems. In this paper, a hybridization of Particle swarm optimization and Genetic algorithm has been proposed with the back propagation learning based Multilayer perceptron neural network. The effectiveness of the proposed algorithm is shown through a no. of simulation steps with the help of the benchmark datasets considered from UCI machine learning repository. The performance of the algorithm is compared with other standard algorithms to show the steadiness and efficiency as well as statically significant.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Kalinga Institute of Industrial Technology University, Bhubaneswar, 751024, Odisha, India

    Chanda Prasad & S. Mohanty

  2. Department of Comp. Science Engineering and Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur, 768018, Odisha, India

    Bighnaraj Naik, Janmenjoy Nayak & H. S. Behera

Authors
  1. Chanda Prasad
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  2. S. Mohanty
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  3. Bighnaraj Naik
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  4. Janmenjoy Nayak
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  5. H. S. Behera
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Corresponding author

Correspondence to Chanda Prasad .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Prasad, C., Mohanty, S., Naik, B., Nayak, J., Behera, H.S. (2015). An Efficient PSO-GA Based Back Propagation Learning-MLP (PSO-GA-BP-MLP) for Classification. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 1. Smart Innovation, Systems and Technologies, vol 31. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2205-7_48

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  • DOI: https://doi.org/10.1007/978-81-322-2205-7_48

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2204-0

  • Online ISBN: 978-81-322-2205-7

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