Improved genetic algorithm inspired by biological evolution

P Kumar, D Gospodaric, P Bauer

    Research output: Contribution to journalArticleScientificpeer-review


    The process of mutation has been studied extensively in the field of biology and it has been shown that it is one of the major factors that aid the process of evolution. Inspired by this a novel genetic algorithm (GA) is presented here. Various mutation operators such as small mutation, gene mutation and chromosome mutation have been applied in this genetic algorithm. In order to facilitate the implementation of the above-mentioned mutation operators a modified way of representing the variables has been presented. It resembles the way genetic information is coded in living beings. Different mutation operators pose a challenge as regards the determination of the optimal rate of mutation. This problem is overcome by using adaptive mutation operators. The main purpose behind this approach was to improve the efficiency of GAs and to find widely distributed Pareto-optimal solutions. This algorithm was tested on some benchmark test functions and compared with other GAs. It was observed that the introduction of these mutations do improve the genetic algorithms in terms of convergence and the quality of the solutions. Keywords Genetic algorithms - Multi-objective optimization - Mutations
    Original languageUndefined/Unknown
    Pages (from-to)923-941
    Number of pages19
    JournalSoft Computing: a fusion of foundations, methodologies and applications
    Issue number10
    Publication statusPublished - 2007


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