The automatic generation of mutation operators for genetic algorithms
John Robert Woodward, Jerry Swan · 2012
We automatically generate mutation operators for Genetic Algorithms (GA) and tune them to problem instances drawn from a given problem class. By so doing, we perform metalearning in which the base-level contains GAs (which learn about problem instances), and the meta-level contains GAmutation operators (which learn about problem classes). We use Register Machines to explore a constrained design space for mutation operators. We show how two commonly used mutation operators (viz. one-point and uniform mutation) can be expressed in this framework. Iterated local search is used to search the space of mutation operators, and on a test-bed of 7 problem classes we identify machine-designed mutation operators which outperform their human counterparts.