Semiclassical genetic algorithm with quantum crossover and mutation operations
Year: 2012
Type: preprint
Abstract: In order for finding a good individual for a given fitness function in the context of evolutionary computing, weintroduce a novel semiclassical quantum genetic algorithm. It has both of quantum crossover and quantum mutationprocedures unlike conventional quantum genetic algorithms. A complexity analysis shows a certain improvementover its classical counterpart. 1 Introduction Continuous development has been performed on genetic algorithms [1–3]. Along with the development of quantumcomputing [4, 5], quantum-inspired classical algorithms for evolutionary computing have been developed [6–13] (seealso a review [14] and references therein). In addition, classical genetic algorithms to evolve quantum circuits have alsobeen studied by several authors [15–29] (see also review articles [30, 31]). These algorithms are, however, designed towork on classical computers. Quantum genetic algorithms (QGAs), in its literal meaning, nonetheless, have gatheredcomparably little attention and a few works [32–36] have been performed so far. Evolutionary computing on quantumarchitectures will achieve more attention if there is a scenario to establish significant improvement over classicalcounterparts. Indeed, Malossini
Source: arXiv (Cornell University)
Authors: Akira Saitoh, Robabeh Rahimi, Mikio Nakahara
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2099801160