Effect of crossover operators under multirecombination: weighted tardiness, a test case

In evolutionary algorithms based on genetics, the crossover operation creates individuals by exchange of genes. Selection mechanisms propitiate reproduction of better individuals replace worst ones. Consequently, part of the genetic material contained in these worst individuals vanishes forever. This loss of diversity can lead to a premature convergence. To prevent an early convergence to a local optimum under the same selection mechanism then, either a large population size or adequate genetic operators are needed. Multirecombination allows multiple crossover operations on two or more parents each time a new individual is created. In this work, we show the influence on genetic diversity, quality of results and required computational effort, when applying different crossover methods to a set of hard instances, selected as a test case, of the weighted tardiness scheduling problem in single machine environments under multirecombined approaches. A description of the multirecombination variant used, experiments and preliminary results are reported.