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
Evolving Quantum Circuits Using Genetic Algorithm
EH '02 Proceedings of the 2002 NASA/DoD Conference on Evolvable Hardware (EH'02)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Quantum Computer Programming: A Genetic Programming Approach (Genetic Programming)
Automatic Synthesis for Quantum Circuits Using Genetic Algorithms
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
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
Genetic algorithm based quantum circuit synthesis with adaptive parameters control
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
Synthesis of reversible logic circuits
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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Setting the values of various parameters for an evolutionary algorithm is essential for its good performance. This paper discusses two optimization strategies that may be used on a conventional Genetic Algorithm to evolve quantum circuits: adaptive (parameters initial values are set before actually running the algorithm) or self-adaptive (parameters change at runtime). The differences between these approaches are investigated, with the focus being put on algorithm performance in terms of evolution time. When taking into consideration the runtime as main target, the performed experiments show that the adaptive behavior (tuning) is more effective for quantum circuit synthesis as opposed to self-adaptive (control). This research provides an answer to whether an evolutionary algorithm applied to quantum circuit synthesis may be more effective when automatic parameter adjustments are made during evolution.