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Teacher: A Genetics Based System for Learning and Generalizing Heuristics

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Soft Computing in Case Based Reasoning

Abstract

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 find, under resource constraints, improved heuristic methods (HMs) as compared to existing ones. Teacher addresses five general issues in learning heuristics: (1) decomposition of a problem solver into smaller components and integration of HMs designed for each together; (2) classification 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 find 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 classification phase, the application domain is divided into subspaces (based on user requirements) and problem subdomains (based on the performance behavior of HMs). In the learning phase, HMs are generated and evaluated under resource constraints with a goal of discovering improved HMs. In the performance-verification phase, good HMs from the learning phase are further evaluated to acquire more accurate and more complete performance information. Finally, in the performance-generalization phase, HMs most likely to provide the best performance over the entire application domain are selected. We conclude the chapter by showing some experimental results on heuristics learned for two problems used in circuit testing.

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Authors
  1. Benjamin W. Wah
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  2. Arthur Ieumwananonthachai
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Editor information

Editors and Affiliations

  1. Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal MTech, PhD, DIC, Fellow IEEE (Distinguished Scientist and Head) (Distinguished Scientist and Head)

  2. Department of Computer Science and Computer Engineering, La Trobe University, Bundoora, Melbourne, Victoria, 3083, Australia

    Tharam S. Dillon BE, PhD, FSaRS, FIE, SMIEEE

  3. Department of Computing, The Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong

    Daniel S. Yeung BA, MA, MSc, MBA, PhD, FHKIE

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Wah, B.W., Ieumwananonthachai, A. (2001). Teacher: A Genetics Based System for Learning and Generalizing Heuristics. In: Pal, S.K., Dillon, T.S., Yeung, D.S. (eds) Soft Computing in Case Based Reasoning. Springer, London. https://doi.org/10.1007/978-1-4471-0687-6_8

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  • DOI: https://doi.org/10.1007/978-1-4471-0687-6_8

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-262-4

  • Online ISBN: 978-1-4471-0687-6

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