Variable Search Space Converging Genetic Algorithm for Solving System of Non-linear Equations

• Main Authors: SS Venkatesh, Mishra Deepak
• Format: Article
• Language: English
• Published: De Gruyter 2020-07-01
• Series: Journal of Intelligent Systems
• Subjects: genetic algorithm; roulette wheel selection; cross over; mutation; multi objective optimization; multi variable optimization; computer science; artificial intelligence
• Online Access: https://doi.org/10.1515/jisys-2019-0233

This paper introduce a new variant of the Genetic Algorithm whichis developed to handle multivariable, multi-objective and very high search space optimization problems like the solving system of non-linear equations. It is an integer coded Genetic Algorithm with conventional cross over and mutation but with Inverse algorithm is varying its search space by varying its digit length on every cycle and it does a fine search followed by a coarse search. And its solution to the optimization problem will converge to precise value over the cycles. Every equation of the system is considered as a single minimization objective function. Multiple objectives are converted to a single fitness function by summing their absolute values. Some difficult test functions for optimization and applications are used to evaluate this algorithm. The results prove that this algorithm is capable to produce promising and precise results.