Optimization of control parameters for genetic algorithms
IEEE Transactions on Systems, Man and Cybernetics
Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Improved low-density subset sum algorithms
Computational Complexity
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Cultural algorithms: theory and applications
New ideas in optimization
Towards Automatic Domain Knowledge Extraction for Evolutionary Heuristics
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Multi-Parent's Niche: n-ary Crossovers on NK-Landscapes
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Maintaining Connectivity in a Scalable and Robust Distributed Environment
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A scalable and robust framework for distributed applications
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Selecting small audio feature sets in music classification by means of asymmetric mutation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
Multi-objective feature selection in music genre and style recognition tasks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
| Hi-index | 0.00 |
This paper discusses a promising new research direction, the automatic learning of algorithm components for problem classes. We focus on the methodology of this research direction. As an illustration, a mutation operator for a special class of subset sum problem instances is learned. The most important methodological issue is the emphasis on the generalisability of the results. Not only a methodology but also a tool is proposed. This tool is called DRM (distributed resource machine), developed as part of the DREAM project, and is capable of running distributed experiments on the Internet making a huge amount of resources available to the researcher in a robust manner. It is argued that the DRM is ideally suited for algorithm learning.