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New evolutionary genetic algorithms for NP-complete combinatorial optimization problems

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Abstract

Evolutionary genetic algorithms have been proposed to solve NP-complete combinatorial optimization problems. A new crossover operator based on group theory has been created. Computational processes motivated by proposed evolutionary genetic algorithms were described as stochastic processes, using population dynamics and interactive markovian chains. The proposed algorithms were used in solving flowshop problems and an asymmetric traveling salesman problem. The experimental results showed the superiority of new evolutionary algorithms in comparison with the standard genetic algorithm.

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Correspondence to Fam Quang Bac.

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Bac, F.Q., Perov, V.L. New evolutionary genetic algorithms for NP-complete combinatorial optimization problems. Biol. Cybern. 69, 229–234 (1993). https://doi.org/10.1007/BF00198963

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Keywords

  • Genetic Algorithm
  • Markovian Chain
  • Stochastic Process
  • Population Dynamic
  • Evolutionary Algorithm