A modified genetic algorithm with a new crossover mating scheme

Abstract

This study introduced the Inversed Bi-segmented Average Crossover (IBAX), a novel crossover operator that enhanced the offspring generation of the genetic algorithm (GA) for variable minimization and numerical optimization problems. An attempt to come up with a new mating scheme in generating new offspring under the crossover function through the novel IBAX operator has paved the way to a more efficient and optimized solution for variable minimization particularly on premature convergence problem using GA. A total of 597 records of student-respondents in the evaluation of the faculty instructional performance, represented by 30 variables, from the four State Universities and Colleges (SUC) in Caraga Region, Philippines were used as the dataset.  The simulation results showed that the proposed modification on the Average Crossover (AX) of the genetic algorithm outperformed the genetic algorithm with the original AX operator. The GA with IBAX operator combined with rank-based selection function has removed 20 or 66.66% of the variables while 13 or 43.33% of the variables were removed when GA with AX operator and roulette wheel selection function was used.