AIM-GP and parallelism

Author

Nordin, Peter ; Hoffmann, Frank ; Francone, Frank D. ; Brameier, Markus ; Banzhaf, Wolfgang

Author_Institution

Phys. Resource Theory, Chalmers Univ. of Technol., Goteborg, Sweden

Volume

2

fYear

1999

fDate

1999

Abstract

Many machine learning tasks are just too hard to be solved with a single processor machine, no matter how efficient the algorithms are and how fast our hardware is. Luckily genetic programming is well suited for parallelization compared to standard serial algorithms. The paper describes the first parallel implementation of an AIM-GP system, creating the potential for an extremely fast system. The system is tested on three problems and several variants of demes and migration are evaluated. Most of the results are applicable to both linear and tree based systems

Keywords

automatic programming; genetic algorithms; learning (artificial intelligence); parallel algorithms; parallel programming; AIM-GP system; Automatic Induction of Machine Code with Genetic Programming; demes; fast system; genetic programming; machine learning tasks; parallel implementation; parallelization; tree based systems; Adaptive systems; Application software; Concurrent computing; Genetic programming; Hardware; Humans; Machine learning; Machine learning algorithms; Parallel processing; System testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on

Conference_Location

Washington, DC

Print_ISBN

0-7803-5536-9

Type

conf

DOI

10.1109/CEC.1999.782540

Filename

782540