Paper Info
Title
Robust Evolution Optimization At The Edge Of Chaos: Commercialization Of Culture Algorithms
Abstract
Robustness is a key concern when developing a successful commercial evolutionary tool. In this paper we investigate the performance of Cultural Algorithms over the complete range of system complexities, from fixed to chaotic. In order to apply the Cultural Algorithm over all complexity classes we generalize on its co-evolutionary nature to keep the variation in the population across all complexities. Based on previous cultural algorithm approaches, we were to extend the existing models to produce a more general one that could be applied across all complexity classes. We then applied the system to the solution of a 150 randomly generated problems that ranged from simple to chaotic complexity classes. As a result we were able to produce the following conclusions: No homogeneous Social Fabric tested was dominant over all categories of complexity. As the complexity of problems increased, so did the complexity of the Social Fabric that was needed to deal with it efficiently. In other words, there was experimental evidence that social structure can be related to the frequency and complexity type of the problems that are presented to a cultural system.
Year
DOI
Venue
2010
10.1109/CEC.2010.5586145
2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
Keywords
Field
DocType
cultural differences,topology,entropy,cultural system,computational complexity,evolutionary computation,social structure,complexity class,network topology
Population,Edge of chaos,Computer science,Artificial intelligence,Complexity class,Mathematical optimization,Evolutionary computation,Algorithm,Descriptive complexity theory,Cultural algorithm,Worst-case complexity,Machine learning,Computational complexity theory
Conference
Citations 
PageRank 
References 
1
0.36
0
Authors
3
Name
Order
Citations
PageRank
Xiangdong Che142.91
Mostafa Z. Ali225219.32
Robert G. Reynolds3610188.20