ABSTRACT
Quantum Genetic Algorithm is a relatively new field of study to enhance the computational efficiency of the Darwinian optimization process in genetic algorithms with quantum speedup techniques. This paper introduces an application strategy of the quantum counting algorithm to genetic algorithms, particularly aimed to enhance the initial population setup at the beginning of optimization. More specifically, our goal is to exploit a quantum algorithm to count the number of marked items from an unstructured list quadratically faster than classical algorithms in order to detect the presence and amount of unsuitable individuals in a stochastically generated initial population, thereby starting optimization with a mark of potential to improve the performance in the later stage. The advantage of our method is examined via a conventional genetic algorithm to solve the 0-1 Knapsack problem with varying cases of the constraints, and a comparative analysis on the optimizing performance is made accordingly.
- Michel Boyer, Gilles Brassard, Peter Høyer, and Alain Tapp. 1998. Tight Bounds on Quantum Searching. Fortschritte der Physik 46, 4--5 (Jun 1998), 493--505. Google ScholarCross Ref
- David E. Goldberg. 1988. Genetic Algorithms in Search, Optimization and Machine Learning (13 ed.). Addison-Wesley Professional.Google Scholar
- Lov K. Grover. 1996. A Fast Quantum Mechanical Algorithm for Database Search. In Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing (Philadelphia, Pennsylvania, USA) (STOC '96). Association for Computing Machinery, New York, NY, USA, 212--219. Google ScholarDigital Library
- Kaggle. 2020. knapsack 2020 | Kaggle. https://www.kaggle.com/c/knapsack2020/submissions/final.json?sortBy=date&group=allGoogle Scholar
- Michael A. Nielsen and Isaac L. Chuang. 2004. Quantum Computation and Quantum Information: 10th Anniversary Edition (1 ed.). Cambridge University Press.Google ScholarDigital Library
- Weifeng Pan, Kangshun Li, Muchou Wang, Jing Wang, and Bo Jiang. 2014. Adaptive Randomness: A New Population Initialization Method. Mathematical Problems in Engineering 2014 (2014).Google Scholar
- Shahryar Rahnamayan, Hamid R. Tizhoosh, and Magdy M.A. Salama. 2007. A novel population initialization method for accelerating evolutionary algorithms. Computers Mathematics with Applications 53, 10 (2007), 1605--1614. Google ScholarDigital Library
- Noson S. Yanofsky and Mirco A. Mannucci. 2008. Quantum Computing for Computer Scientists (1 ed.). Cambridge University Press.Google Scholar
Index Terms
- Quantum strategy of population initialization in genetic algorithm
Recommendations
Hybrid genetic algorithm based on quantum computing for numerical optimization and parameter estimation
Quantum computing is applied to genetic algorithm (GA) to develop a class of quantum-inspired genetic algorithm (QGA) characterized by certain principles of quantum mechanisms for numerical optimization. Furthermore, a framework of hybrid QGA, named ...
A quantum genetic algorithm with quantum crossover and mutation operations
In the context of evolutionary quantum computing in the literal meaning, a quantum crossover operation has not been introduced so far. Here, we introduce a novel quantum genetic algorithm that has a quantum crossover procedure performing crossovers ...
An improved genetic algorithm with conditional genetic operators and its application to set-covering problem
The genetic algorithm (GA) is a popular, biologically inspired optimization method. However, in the GA there is no rule of thumb to design the GA operators and select GA parameters. Instead, trial-and-error has to be applied. In this paper we present an ...
Comments