A New Genetic Improvement Operator Based on Frequency Analysis for Genetic Algorithms Applied to Job Shop Scheduling Problem.

Many researchers today are using meta-heuristics to treat the class of problems known in the literature as Job Shop Scheduling Problem (JSSP) due to its complexity since it consists of combinatorial problems and it is an NP-Hard computational problem. JSSPs are a resource allocation issue and, to solve its instances, meta-heuristics as Genetic Algorithm (GA) are widely used. Although the GAs present good results in the literature, it is very common for these methods that they are stagnant in solutions that are local optima during their iterations and that have difficulty in adequately exploring the search space. To circumvent these situations, we propose in this work the use of an operator specialized in conducting the GA population to a good exploration: the Genetic Improvement based on Frequency Analysis (GIFA). GIFA makes it possible to manipulate the genetic material of individuals by adding characteristics that are believed to be important, with the proposal of directing some individuals who are lost in the search space to a more favorable subspace without breaking the diversity of the population. The proposed GIFA is evaluated considering two different situations in well-established benchmarks in the specialized JSSP literature and proved to be competitive and robust compared to the methods that represent the state of the art.