IMPROVED CULTURAL ALGORITHMS FOR JOB SHOP SCHEDULING PROBLEM

This paper presents a new cultural algorithm for job shop scheduling problem. Unlike the canonical genetic algorithm, in which random elitist selection and mutational genetics is assumed. The proposed cultural algorithm extract the useful knowledge from the population space of genetic algorithm to form belief space, and utilize it to guide the genetic operator of selection and mutation. The different sizes of the benchmark data taken from literature are used to analyze the efficacy of this algorithm. Experimental results indicate that it outperforms current approaches using canonical genetic algorithms in computational time and quality of the solutions.