Teacher: A Genetics Based System for Learning and Generalizing Heuristics

Wah, Benjamin W.; Ieumwananonthachai, Arthur

doi:10.1007/978-1-4471-0687-6_8

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This study is unique in the literature, as the automated approach is compared against those produced by three NASA programmers, producing competitive results, and even often outperforming the human programmers. Another interesting learning approach in the mid-1990s was termed 'Teacher' (Wah et al, 1995;Wah and Ieumwananonthachai, 1999) (an acronym for TEchniques for the Automated Creation of HEuRistics), which was designed as a system for learning and generalising heuristics used in problem solving. The objective was to find improved heuristic methods as compared with existing ones, in applications with little or non-existent domain knowledge.…”

Section: Automated Learning Of Heuristic Methodsmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

970

550

View full text Add to dashboard Cite

Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

show abstract

Section: Automated Learning Of Heuristic Methodsmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

970

550

View full text Add to dashboard Cite

show abstract

“…Probabilities of mean have been used to evaluate generalizability in various genetics-based learning and generalization experiments [39], [15], [34], [24], [41], [40], [16], [38]. These include the learning of load balancing strategies in distributed systems and multicomputers, the tuning of parameters in VLSI cell placement and routing, the tuning of fitness functions in genetics-based VLSI circuit testing, the automated design of feedforward neural networks, the design of heuristics in branch-and-bound search, range estimation in stereo vision, and the learning of parameters for blind equalization in signal processing.…”

Section: ) No Hypothesis Is Better Than H 0 In All Subdomainsmentioning

confidence: 99%

Generalization and generalizability measures

Wah

1999

IEEE Trans. Knowl. Data Eng.

View full text Add to dashboard Cite

In this paper, we define the generalization problem, summarize various approaches in generalization, identify the credit assignment problem, and present the problem and some solutions in measuring generalizability. We discuss anomalies in the ordering of hypotheses in a subdomain when performance is normalized and averaged, and show conditions under which anomalies can be eliminated. To generalize performance across subdomains, we present a measure called probability of win that measures the probability whether one hypothesis is better than another. Finally, we discuss some limitations in using probabilities of win and illustrate their application in finding new parameter values for TimberWolf, a package for VLSI cell placement and routing. Index Terms-Anomalies in generalization, credit assignment problem generalization, machine learning, subdomains, probability of win, VLSI cell placement and routing.

show abstract

A Case-Based Reasoning Approach to Formulating University Timetables Using Genetic Algorithms

Grech

Main

2005

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Teacher: A Genetics Based System for Learning and Generalizing Heuristics

Cited by 3 publications

References 33 publications

Hyper-heuristics: a survey of the state of the art

Hyper-heuristics: a survey of the state of the art

Generalization and generalizability measures

A Case-Based Reasoning Approach to Formulating University Timetables Using Genetic Algorithms

Contact Info

Product

Resources

About