Paper Info
Title
Performance Evaluation of Genetic Algorithms and Evolutionary Programming in Optimization and Machine Learning
Abstract
Genetic Algorithms (GAs) and Evolutionary Programming (EP) are investigated here in both optimization and machine learning. Adaptive and standard versions of the two algorithms are used to solve novel applications in search and rule extraction. Simulations and analysis show that while both algorithms may look similar in many ways their performance may differ for some applications. Mathematical modeling helps in gaining better understanding for GA and EP applications. Proper tuning and loading is a key for acceptable results. The ability to instantly adapt within an unpredictable and unstable search or learning environment is the most important feature of evolution-based techniques such as GAs and EP.
Year
DOI
Venue
2002
10.1080/019697202753551611
CYBERNETICS AND SYSTEMS
Keywords
Field
DocType
genetic algorithm,evolutionary programming,machine learning,mathematical model
Computer science,Genetic programming,Learning environment,Genetic representation,Artificial intelligence,Generalization error,Computational learning theory,Evolutionary programming,Genetic algorithm,Machine learning,Learning classifier system
Journal
Volume
Issue
ISSN
33.0
3
0196-9722
Citations 
PageRank 
References 
1
0.36
8
Authors
2
Name
Order
Citations
PageRank
Raed Abu Zitar18710.95
A. M. Al-Fahed Nuseirat2153.03