T, HashniT, Amudha2020-09-152020-09-152012-020975-833Xhttps://www.journalcra.com/sites/default/files/issue-pdf/1684.pdfhttps://dspace.psgrkcw.com/handle/123456789/1525Nature provides motivation to scientists in many ways. Scientists have started to realize that nature is a great source ofinspiration to develop intelligent systems and techniques. Nature- Inspired algorithms is a kind of algorithms that imitate theproblem-solving behavior from nature. Consultant Guided Search algorithm (CGS) and Genetic algorithm (GA) are some of theNature-Inspired Metaheuristic Algorithms inspired from Nature. In this paper, Consultant Guided Search algorithm (CGS) washybridized with Genetic algorithm (GA) and a new technique was proposed. The proposed Consultant Guided Search – Geneticalgorithm (CGS-GA) was implemented to solve the benchmark instances of Quadratic Assignment Problem (QAP). Theperformance of the proposed CGS-GA was compared with CGS algorithm. Results have shown that the proposed CGS-GA hasoutperformed CGS in arriving at improved optimal solutions for various test instances of Quadratic Assignment ProblemenNature- Inspired algorithmsConsultant Guided Search algorithmGenetic algorithmConsultant Guided Search -Genetic algorithmArticle