Survey of current speech technology
Communications of the ACM
Machine Learning
Machine Learning
Implicit Formae in Genetic Algorithms
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Operator Learning for a Problem Class in a Distributed Peer-to-Peer Environment
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A Time-Sensitive System for Black-Box Combinatorial Optimization
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
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Domain knowledge is essential for successful problem solving and optimization. This paper introduces a framework in which a form of automatic domain knowledge extraction can be implemented using concepts from the field of machine learning. The result is an encoding of the type used in most evolutionary computation (EC) algorithms. The approach focuses on whole problem domains instead of single problems. After the theoretical validation of the algorithm the main idea is given impetus by showing that on different subdomains of linear functions the method finds different encodings which result in different problem complexities.