Using adaptive operator scheduling on problem domains with an operator manifold: applications to the travelling salesman problem

TL;DR: This work investigates the use of adaptive operator scheduling to automate the operator selection process and shows that benefits are twofold: Operator selection is achieved automatically and an overall performance improvement is observed.

Abstract: A growing problem in the field of evolutionary computation is the large amount of genetic operators available for certain problem domains. This tendency is especially pronounced in areas where heuristics are used to create highly specialised operators. Even within the same problem domain, the performance of such operators often depends on the specific problem instance at hand. This results in a tedious and time-consuming process of comparing individual operator performances every time a new problem is to be solved. We investigate the use of adaptive operator scheduling to automate the operator selection process. The approach is tested on instances of the travelling salesman problem - a problem for which a long list of operators exists. Results show that benefits are twofold: Operator selection is achieved automatically and an overall performance improvement is observed.