  The genetic algorithm (GA) is an optimization and search technique based on
the principles of genetics and natural selection. A GA allows a population
composed of many individuals to evolve under specified selection rules to a
state that maximizes the "fitness" function. In that process, crossover
operator plays an important role. To comprehend the GAs 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 GAs. However, how
to decide what operator to use for solving a problem? A number of test
functions with various levels of difficulty has been selected as a test polygon
for determine the performance of crossover operators. In this paper, a novel
crossover operator called 'ring crossover' is proposed. In order to evaluate
the efficiency and feasibility of the proposed operator, a comparison between
the results of this study and results of different crossover operators used in
GAs is made through a number of test functions with various levels of
difficulty. Results of this study clearly show significant differences between
the proposed operator and the other crossover operators.
