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A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems: A GPQIEA for Subset Selection

A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems: A GPQIEA for Subset Selection

Sulabh Bansal, C. Patvardhan
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 38
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781522565710|DOI: 10.4018/IJAEC.2019100101
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MLA

Bansal, Sulabh, and C. Patvardhan. "A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems: A GPQIEA for Subset Selection." IJAEC vol.10, no.4 2019: pp.1-38. http://doi.org/10.4018/IJAEC.2019100101

APA

Bansal, S. & Patvardhan, C. (2019). A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems: A GPQIEA for Subset Selection. International Journal of Applied Evolutionary Computation (IJAEC), 10(4), 1-38. http://doi.org/10.4018/IJAEC.2019100101

Chicago

Bansal, Sulabh, and C. Patvardhan. "A Generalized Parallel Quantum Inspired Evolutionary Algorithm Framework for Hard Subset Selection Problems: A GPQIEA for Subset Selection," International Journal of Applied Evolutionary Computation (IJAEC) 10, no.4: 1-38. http://doi.org/10.4018/IJAEC.2019100101

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Abstract

Quantum-inspired evolutionary algorithms (QIEAs) like all evolutionary algorithms (EAs) perform well on many problems but cannot perform equally better than random for all problems due to the No Free Lunch theorem. However, a framework providing near-optimal solutions on reasonably hard instances of a large variety of problems is feasible. It has an effective general strategy for easy incorporation of domain information along with effective control on the randomness in the search process to balance the exploration and exploitation. Moreover, its effective parallel implementation is desired in the current age. Such a Generalized Parallel QIEA framework designed for the solution of Subset Selection Problems is presented here. The computational performance results demonstrate its effectiveness in the solution of different large-sized hard SSPs like the Difficult Knapsack Problem, the Quadratic Knapsack Problem and the Multiple Knapsack problem. This is the first such a generalized framework and is a major step towards creating an adaptive search framework for combinatorial optimization problems.

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