Using genetic algorithms with asexual transposition
Anabela Simões, Ernesto J. F. Costa · 2000
Traditional Genetic Algorithms (GA) use crossover and mutation as the main genetic operators to achieve population diversity. Previous work using a biologically inspired genetic operator called transposition, allowed the GA to reach better solutions by replacing the traditional crossover operators. In this paper we extend that work to the case of asexual reproduction. The GA efficiency was compared when using asexual transposition and the classical crossover operators. The results obtained show that asexual transposition still allowed the modified GA to achieve higher performances. 1 INTRODUCTION Genetic diversity is essential for the evolutionary process. When using genetic algorithms, a population evolves through the application of two main genetic operators: mutation and crossover. These operators allow changes in the individuals, creating evolutionary advantages in some of them. The fittest individuals are more likely to be selected allowing the evolution of the popu...