Open AccessA genetic algorithm rooted in integer encoding and fuzzy controller
Author(s) -
M. Jalali Varnamkhasti
Publication year - 2019
Publication title -
international journal of robotics and automation (ijra)/iaes international journal of robotics and automation
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.