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dc.contributor.authorTirumala, Sreenivas Sremath
dc.description.abstractContinued and rapid improvement in evolutionary algorithms has made them suitable technologies for tackling many difficult optimization problems. Recently the introduction of quantum inspired evolutionary computation has opened a new direction for further enhancing the effectiveness of these algorithms. Existing studies on quantum inspired algorithms focused primarily on evolving a single set of homogeneous solutions. This thesis expands the scope of current research by applying quantum computing principles, in particular the quantum superposition principle, to competitive coevolution algorithms (CCEA) and proposes a novel Quantum inspired Competitive Coevolutionary Algorithm (QCCEA). QCCEA uses a new approach to quantize candidate solution unlike previous quantum evolutionary algorithms that use qubit representation. The proposed QCCEA quantifies the selection procedure using normal distribution, which empowers the algorithm to reach the optimal fitness faster than original CCEA. QCCEA is evaluated against CCEA on twenty benchmark numerical optimization problems. The experimental results show that QCCEA performed significantly better than CCEA for most benchmark functions.en_NZ
dc.rightsAll rights reserveden_NZ
dc.subjectquantum computingen_NZ
dc.subjectevolutionary algorithmsen_NZ
dc.subjectcompetitive coevolutionen_NZ
dc.subjectquantum inspireden_NZ
dc.titleA quantum inspired competitive coevolution evolutionary algorithmen_NZ
dc.typeMasters Thesisen_NZ
dc.rights.holderAuthoren_NZ of Computingen_NZ Institute of Technologyen_NZ
dc.subject.marsden080108 Neural, Evolutionary and Fuzzy Computationen_NZ
dc.identifier.bibliographicCitationTirumala, S. S. (2013). A quantum inspired competitive coevolution evolutionary algorithm. (Unpublished document submitted in partial fulfilment of the requirements for the degree of Master of Computing). Unitec Institute of Technology. Retrieved from
dc.contributor.affiliationUnitec Institute of Technologyen_NZ
unitec.advisor.principalPang, Paul
unitec.advisor.associatedChen, Aaron

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