A genetic algorithm rooted in integer encoding and fuzzy controller | Zendy

Mohammad Jalali Varnamkhasti | Zendy

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A genetic algorithm rooted in integer encoding and fuzzy controller

Author(s) -

Mohammad Jalali Varnamkhasti

Publication year - 2019

Publication title -

iaes international journal of robotics and automation (ijra)

Language(s) - English

Resource type - Journals

eISSN - 2722-2586

pISSN - 2089-4856

DOI - 10.11591/ijra.v8i2.pp113-124

Subject(s) - premature convergence , selection (genetic algorithm) , benchmark (surveying) , heuristic , fuzzy logic , genetic algorithm , quality control and genetic algorithms , encoding (memory) , chromosome , computer science , algorithm , convergence (economics) , population , mathematical optimization , controller (irrigation) , mathematics , artificial intelligence , meta optimization , biology , genetics , demography , geodesy , sociology , economic growth , agronomy , economics , gene , geography

The premature convergence is the essential problem in genetic algorithms and it is strongly related to the loss of genetic diversity of the population. In this study, a new sexual selection mechanism which utilizing mate chromosome during selection proposed and then technique focuses on selecting and controlling the genetic operators by applying the fuzzy logic controller. Computational experiments are conducted on the proposed techniques and the results are compared with some other operators, heuristic and local search algorithms commonly used for solving benchmark problems published in the literature.

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