Comparison of a crossover operator in binary-coded genetic algorithms
Stjepan Picek, Marin Golub · WSEAS Transactions on Computers archive · 2010
Genetic algorithms (GAs) represent a method that mimics the process of natural evolution in effort to find good solutions. In that process, crossover operator plays an important role. To comprehend the genetic algorithms as a whole, it is necessary to understand the role of a crossover operator. Today, there are a number of different crossover operators that can be used in binary-coded GAs. How to decide what operator to use when solving a problem? When dealing with different classes of problems, crossover operators will show various levels of efficiency in solving those problems. A number of test functions with various levels of difficulty has been selected as a test polygon for determine the performance of crossover operators. The aim of this paper is to present a larger set of crossover operators used in genetic algorithms with binary representation and to draw some conclusions about their efficiency. Results presented here confirm the high-efficiency of uniform crossover and two-point crossover, but also show some interesting comparisons among others, less used crossover operators.