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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.

Software intensive systems are increasingly becoming complex and difficult to maintain the system structure and its understandability due to extremely dynamic requirements. Automatic software clustering is an important research area in the software reverse engineering domain, which addresses this issue by decomposing the system into multiple subsystems (clusters) with related interdependent components for better understandability and manageability. Researchers have applied meta-heuristics to obtain near optimal solution considering this problem as a graph partitioning problem which is a NP-hard problem. In this work a novel objective function called "Enhanced Turbo MQ (ETMQ)" is proposed and GA is applied and its effectiveness is ascertained for medium size software systems.