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Quantum inspired Evolutionary Algorithms were proposed more than a decade agoand have been employed for solving a wide range of difficult search andoptimization problems. A number of changes have been proposed to improveperformance of canonical QEA. However, canonical QEA is one of the fewevolutionary algorithms, which uses a search operator with relatively largenumber of parameters. It is well known that performance of evolutionaryalgorithms is dependent on specific value of parameters for a given problem.The advantage of having large number of parameters in an operator is that thesearch process can be made more powerful even with a single operator withoutrequiring a combination of other operators for exploration and exploitation.However, the tuning of operators with large number of parameters is complex andcomputationally expensive. This paper proposes a novel heuristic method fortuning parameters of canonical QEA. The tuned QEA outperforms canonical QEA ona class of discrete combinatorial optimization problems which, validates thedesign of the proposed parameter tuning framework. The proposed framework canbe used for tuning other algorithms with both large and small number of tunableparameters.

Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method