A Random Search and Greedy Selection based Genetic Quantum Algorithm for Combinatorial Optimization

Genetic Quantum Algorithm (GQA) is an evolutionary algorithm in the class of quantum inspired evolutionary algorithms inspired by the principles of quantum computing such as Q-bits, super position, quantum gates, interference and coherence. GQA adopts Q-bit representation and applies quantum rotation gate (QR gate) as genetic operator. The performance of the quantum inspired evolutionary algorithms largely depends upon the effectiveness of quantum gates applied as the genetic operator. Researchers have attempted to improve the performance of quantum inspired evolutionary algorithms by designing various quantum evolutionary operators using different strategies. In this paper, an effort is made to study the impact of Random search based QR gate strategy in GQA, and subsequently a Random search and greedy selection based Genetic Quantum Algorithm (RSGS-GQA) is proposed. The performance of RSGS-GQA algorithm is compared with the standard quantum inspired evolutionary algorithms (QIEA) on knapsack problem. The results indicate that, the RSGS-GQA algorithm performs better than the standard QIEA variants in terms of the quality of the solution and convergence.