Design patterns: elements of reusable object-oriented software
Design patterns: elements of reusable object-oriented software
Machine Learning - Special issue on inductive transfer
Learning to learn
Evolving Evolutionary Algorithms Using Linear Genetic Programming
Evolutionary Computation
Computers and Industrial Engineering
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Unbiased black box search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Automatically designing selection heuristics
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
A multistage approach to cooperatively coevolving feature construction and object detection
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Using supportive coevolution to evolve self-configuring crossover
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Evolving black-box search algorithms employing genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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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.