Title :
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
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;
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
DOI :
10.1109/CEC.1999.782540
