Fitness level based adaptive operator selection for cutting stock problems with contiguity

In this article, we propose the Fitness Level based Adaptive Operator Selection (FLAOS). In FLAOS, the discovered objective values are divided into intervals, the fitness levels. A probability distribution corresponding to a fitness level describes the selection probabilities of a set of operators. An evolutionary algorithm with FLAOS is suggested to solve one-dimensional cutting stock problems (CSPs) with contiguity. These problems are bi-objective and the goals are to minimize the trim loss and to minimize the number of partially finished items. Experimental studies have been carried out to test the effectiveness of the FLAOS. The solutions found by FLAOS are better than or comparable to those solutions found by previous methods.