Paper Info
Title
Performance evaluation of evolutionary algorithms for road detection
Abstract
In this paper we present the first comparative study of evolutionary classifiers for the problem of road detection. We use seven evolutionary algorithms (GAssist-ADI, XCS, UCS, cAnt, EvRBF,Fuzzy-AB and FuzzySLAVE) for this purpose and to develop better understanding we also compare their performance with two well-known non-evolutionary classifiers (kNN, C4.5). Further we identify vision based features that enable a single classifier to learn to successfully classify a variety of regions in various roads as opposed to training a new classifier for each type of road. For this we collect a real-world dataset of road images of various roads taken at different times of the day. Then, using Information Gain (I.G) and CfsSubsetMerit values we evaluate the efficacy of our features in facilitating the detection. Our results indicate that intelligent features coupled with right evolutionary technique provides a promising solution for the domain of road detection.
Year
DOI
Venue
2010
10.1145/1830483.1830728
GECCO
Keywords
Field
DocType
information gain,single classifier,right evolutionary technique,evolutionary classifier,well-known non-evolutionary classifier,evolutionary algorithm,performance evaluation,various road,road detection,road image,new classifier
Evolutionary algorithm,Computer science,Information gain,Vision based,Artificial intelligence,Classifier (linguistics),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
4
Authors
5
Name
Order
Citations
PageRank
Muhammad Jamal Afridi1172.38
Salman Manzoor291.01
Umer Rasheed3242.27
Mariam Ahmed461.48
Faraz Kunwar5254.80