Paper Info
Title
Bio-inspired Training Algorithms for Artificial Hydrocarbon Networks: A Comparative Study
Abstract
Artificial hydrocarbon networks (AHN) is a supervised learning algorithm inspired on chemical organic compounds. Its first implementation occupied the well-known least squares estimates (LSE) as part of the training algorithm. Unsurprisingly, AHN cannot converge to suitable solutions when dealing with high dimensional data, falling into the curse of dimensionality. In that sense, this paper proposes two hybrid training algorithms for AHN using bio-inspired algorithms, i.e. Simulated annealing and particle swarm optimization, and compares them against the LSE-based method. Experimental results show that these bio-inspired algorithms improve the performance of artificial hydrocarbon networks, concluding that these hybrid algorithms can be used as alternative learning algorithms for high dimensional data.
Year
DOI
Venue
2014
10.1109/MICAI.2014.31
2014 13th Mexican International Conference on Artificial Intelligence
Keywords
Field
DocType
artificial hydrocarbon networks,simulated annealing,particle swarm optimization,bio-inspired algorithms
Particle swarm optimization,Simulated annealing,Least squares,Clustering high-dimensional data,Computer science,Algorithm,Curse of dimensionality,Artificial intelligence,Supervised training,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4673-7010-3
0
0.34
References 
Authors
2
1
Name
Order
Citations
PageRank
Hiram E. Ponce12613.63