Seed space and version space: Generalizing from approximations

Version space controlled genetic algorithms (VGA)

[1991] Proceedings. The Second Annual Conference on AI, Simulation and Planning in High Autonomy Systems ◽

10.1109/aihas.1991.138440 ◽

2002 ◽

Cited By ~ 2

Author(s):

R.G. Reynolds

Keyword(s):

Genetic Algorithms ◽

Version Space

Learning Concise Pattern for Interlinking with Extended Version Space

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) ◽

10.1109/wi-iat.2014.18 ◽

2014 ◽

Cited By ~ 1

Author(s):

Zhengjie Fan ◽

Jerome Euzenat ◽

Francois Scharffe

Keyword(s):

Extended Version ◽

Version Space

10.1007/springerreference_179589 ◽

2012 ◽

Keyword(s):

Version Space

Version space algorithms on hierarchies with exceptions

Progress in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/3-540-57287-2_43 ◽

1993 ◽

pp. 136-149 ◽

Cited By ~ 1

Author(s):

Grigoris Antoniou

Keyword(s):

Version Space

Version-space induction with multiple concept languages

Trends in Artificial Intelligence - Lecture Notes in Computer Science ◽

10.1007/3-540-54712-6_232 ◽

1991 ◽

pp. 197-207

Author(s):

Claudio Carpineto

Keyword(s):

Version Space ◽

Concept Languages

Incremental Version-Space Merging: A General Framework for Concept Learning

10.1007/978-1-4613-1557-5 ◽

1990 ◽

Cited By ~ 23

Author(s):

Haym Hirsh

Keyword(s):

Concept Learning ◽

General Framework ◽

Version Space

A Factorized Version Space Algorithm for "Human-In-the-Loop" Data Exploration

2019 IEEE International Conference on Data Mining (ICDM) ◽

10.1109/icdm.2019.00117 ◽

2019 ◽

Author(s):

Luciano Di Palma ◽

Yanlei Diao ◽

Anna Liu

Keyword(s):

Data Exploration ◽

Human In The Loop ◽

Version Space

Elastic version space: a knowledge acquisition method with background knowledge adjustment

Proceedings of 1993 IEEE Conference on Tools with Al (TAI-93) ◽

10.1109/tai.1993.633958 ◽

2002 ◽

Author(s):

K.-I. Hagiwara

Keyword(s):

Knowledge Acquisition ◽

Background Knowledge ◽

Version Space ◽

Acquisition Method

A generalized version space learning algorithm for noisy and uncertain data

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/69.591457 ◽

1997 ◽

Vol 9 (2) ◽

pp. 336-340 ◽

Cited By ~ 36

Author(s):

Tzung-Pai Hong ◽

Shian-Shyong Tsang

Keyword(s):

Learning Algorithm ◽

Uncertain Data ◽

Version Space

THE USE OF VERSION SPACE CONTROLLED GENETIC ALGORITHMS TO SOLVE THE BOOLE PROBLEM

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213093000126 ◽

1993 ◽

Vol 02 (02) ◽

pp. 219-234 ◽

Cited By ~ 10

Author(s):

ROBERT G. REYNOLDS ◽

JONATHAN I. MALETIC

Keyword(s):

Genetic Algorithm ◽

Genetic Algorithms ◽

Decision Trees ◽

General Framework ◽

Performance History ◽

New Members ◽

Symbiotic Relationships ◽

Version Space ◽

History Of ◽

Better Than

The Version Space Controlled Genetic Algorithms (VGA) uses the structure of the version space to cache generalizations about the performance history of chromosomes in the genetic algorithm. This cached experience is used to constrain the generation of new members of the genetic algorithms population. The VGA is shown to be a specific instantiation of a more general framework, Autonomous Learning Elements (ALE). The capabilities of the VGA system are demonstrated using the Boole problem suggested by Wilson [Wilson 1987]. The performance of the VGA is compared to that of decision trees and genetic algorithms. The results suggest that the VGA is able to exploit a certain set of symbiotic relationships between its components, so that the resulting system performs better than either component individually.