Teacher: a Genetics-based System for Learning and for Generalizing Heuristics
نویسندگان
چکیده
In this chapter, we present the design of Teacher (an acronym for TEchniques for the Automated Creation of HEuRistics), a system for learning and for generalizing heuristics used in problem solving. Our system learns knowledge-lean heuristics whose performance is measured statistically. The objective of the design process is to nd, under resource constraints, improved heuristic methods (HMs) as compared to existing ones. Teacher addresses ve general issues in learning heuris-tics: (1) decomposition of a problem solver into smaller components and integration of HMs designed for each together; (2) classiication of an application domain into subdomains so that performance can be evaluated statistically for each; (3) generation of new and improved HMs based on past performance information and heuristics generated; (4) evaluation of each HM's performance; and (5) performance generalization to nd HMs that perform well across the entire application domain. Teacher employs a genetics-based machine learning approach and divides the design process into four phases. In the classiication phase,
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