A real coded genetic algorithm for solving integer and mixed integer optimization problems

  • Deep K
  • Singh K
  • Kansal M
 et al. 
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Abstract

In this paper, a real coded genetic algorithm named MI-LXPM is proposed for solving integer and mixed integer constrained optimization problems. The proposed algorithm is a suitably modified and extended version of the real coded genetic algorithm, LXPM, of Deep and Thakur [K. Deep, M. Thakur, A new crossover operator for real coded genetic algorithms, Applied Mathematics and Computation 188 (2007) 895-912; K. Deep, M. Thakur, A new mutation operator for real coded genetic algorithms, Applied Mathematics and Computation 193 (2007) 211-230]. The algorithm incorporates a special truncation procedure to handle integer restrictions on decision variables along with a parameter free penalty approach for handling constraints. Performance of the algorithm is tested on a set of twenty test problems selected from different sources in literature, and compared with the performance of an earlier application of genetic algorithm and also with random search based algorithm, RST2ANU, incorporating annealing concept. The proposed MI-LXPM outperforms both the algorithms in most of the cases which are considered. © 2009 Elsevier Inc. All rights reserved.

Author-supplied keywords

  • Constrained optimization
  • Integer and mixed integer optimization problems
  • Random search based techniques
  • Real coded genetic algorithms

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Authors

  • Kusum Deep

  • Krishna Pratap Singh

  • M. L. Kansal

  • C. Mohan

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