Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An adaptive crossover distribution mechanism for genetic algorithms
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the sixth international workshop on Machine learning
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Biases in the Crossover Landscape
Proceedings of the 3rd International Conference on Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
An overview of evolutionary algorithms for parameter optimization
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
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
Comparison of a crossover operator in binary-coded genetic algorithms
WSEAS Transactions on Computers
A genetic fuzzy rules learning approach for unseeded segmentation in echography
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Countering the negative search bias of ant colony optimization in subset selection problems
Computers and Operations Research
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The traditional crossover operator used in genetic search exhibits a position-dependent bias called the dcfining-length bias. We show how this bias results in hidden biases that are difficult to anticipate and compensate for. We introduce a new crossover operator, shuffle crossover, that eliminates the position dependent bias of the traditional crossover operator by shuffling the representation prior to applying crossover. We also present experimental results that show that shuffle crossover outperforms traditional crossover on a suite of five function optimization problems.