Binary gravitational search algorithm (BGSA) for solving feature selection problem

  • Authors

    • Nageswara Rao Banoth
    • Suresh Dara y
    • M Jagadeeshwara Reddy
    • R P. Singh
    https://doi.org/10.14419/ijet.v7i3.29.19310
  • Gravitational Search Algorithm, Evolutionary Algorithms, Law of Gravity, Optimization.

  • Abstract

    In previous years, dierent Lateral thinking optimization techniques have been developed based on evolutionary computation. Many of these methods are inspired by spill out behaviors in nature. In this Paper, a new optimization algorithm based on the law of gravity and mass interactions named as Gravitational Search Algorithm (GSA) is discussed for solving feature selection. In GSA, the searcher agents are a collection of masses which will interact with each other based on the law of motion and Newtonian gravity which gives the binary evolutionary optimized high performance. The detailed feature selection has been discussed in this paper and The GSA method has been compared with some wellknown optimized search methods such as GA (Genetic Algorithm), PSO (Particle Swarm Optimization).

     

     

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  • How to Cite

    Rao Banoth, N., Dara y, S., Jagadeeshwara Reddy, M., & P. Singh, R. (2018). Binary gravitational search algorithm (BGSA) for solving feature selection problem. International Journal of Engineering & Technology, 7(3.29), 554-557. https://doi.org/10.14419/ijet.v7i3.29.19310

    Received date: 2018-09-09

    Accepted date: 2018-09-09