Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Evolutionary computation
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Dealings with problem hardness in genetic algorithms
WSEAS Transactions on Computers
The new negative slope coefficient measure
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
On the efficiency of crossover operators in genetic algorithms with binary representation
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
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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.