Programming model for concept learning and its solution based on genetic algorithms

Author

MinQiang, Li ; Jisong, Kou ; Jing, Zhou

Author_Institution

Inst. of Syst. Eng., Tianjin Univ., China

Volume

1

fYear

2000

fDate

2000

Firstpage

299

Abstract

Learning from examples is an important branch of inductive learning, and is also the bottleneck in concepts extraction of machine learning. Based on inductive learning theory this paper applies combinatorial optimization method to setup the programming models of learning concepts of the prepositional logic formulas in the conjunctive normal form (CNF) and disjunctive normal form (DNF). Then, genetic algorithms (GA), specified to CNF learning, is designed. GA can find the multiple optimal solution in theory and practice, and experiments reveal that it runs more efficiently compared with heuristic algorithms of the generalisation-and-specialisation (GS) type

Keywords

combinatorial mathematics; genetic algorithms; learning (artificial intelligence); CNF; DNF; GA; GS algorithms; combinatorial optimization method; concept learning; conjunctive normal form; disjunctive normal form; generalisation-and-specialisation algorithms; genetic algorithms; heuristic algorithms; inductive learning theory; machine learning; multiple optimal solution; prepositional logic formulas; Educational programs; Educational technology; Genetic algorithms; Genetic engineering; Logic programming; Machine learning; Optimization methods; Programming profession; Systems engineering and theory; Systems engineering education;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on

Conference_Location

Hefei

Print_ISBN

0-7803-5995-X

Type

conf

DOI

10.1109/WCICA.2000.859970

Filename

859970