An empirical study of genetic operators in genetic algorithms
EUROMICRO 93 Nineteenth EUROMICRO symposium on microprocessing and microprogramming on Open system design : hardware, software and applications: hardware, software and applications
Quantum computation and quantum information
Quantum computation and quantum information
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Quantum Circuit Synthesis with Adaptive Parameters Control
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Comparing parameter tuning methods for evolutionary algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Automated operator selection on genetic algorithms
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
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Genetic algorithms have proven their ability in detecting optimal or closed-to-optimal solutions to hard combinational problems. However, determining which crossover, mutation or selector operator is best for a specific problem can be cumbersome. The possibilities for enhancing genetic operators are discussed herein, starting with an analysis of their run-time performance. The contribution of this paper consist of analyzing the performance gain from the dynamic adjustment of the genetic operators, with respect to overall performance, as applied for the task of quantum circuit synthesis. We provide experimental results demonstrating the effectiveness of the approach by comparing our results against a traditional GA, using statistical significance measurements.