Cross-disciplinary perspectives on meta-learning for algorithm selection
ACM Computing Surveys (CSUR)
Practical performance models of algorithms in evolutionary program induction and other domains
Artificial Intelligence
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Different successful heuristic approaches have been proposed for solving combinatorial optimization problems. Commonly, each of them is specialized to serve a different purpose or address specific difficulties. However, most combinatorial problems that model real world applications have a priori well known measurable properties. Embedded machine learning methods may aid towards the recognition and utilization of these properties for the achievement of satisfactory solutions. In this paper, we present a heuristic methodology which employs the instance-based machine learning paradigm. This methodology can be adequately configured for several types of optimization problems which are known to have certain properties. Experimental results are discussed concerning two well known problems, namely the knapsack problem and the set partitioning problem. These results show that the proposed approach is able to find significantly better solutions compared to intuitive search methods based on heuristics which are usually applied to the specific problems.