Improved genetic algorithm inspired by biological evolution |
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Authors: | P Kumar D Gospodaric P Bauer |
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Affiliation: | (1) Trimerics, 70794 Filderstadt, Germany;(2) Trimerics GmbH, 70794 Filderstadt, Germany;(3) Delft University of Technology, Delft, The Netherlands |
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Abstract: | 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. |
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Keywords: | Genetic algorithms Multi-objective optimization Mutations |
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