Authors:
Nuno Leite
1
;
Fernando Melício
2
and
Agostinho Rosa
2
Affiliations:
1
Instituto Superior de Engenharia de Lisboa/ADEETC and Universidade de Lisboa, Portugal
;
2
Universidade de Lisboa, Portugal
Keyword(s):
Cellular Genetic Algorithms, Clustering, Classification, Evolutionary Computation, Nature Inspired Algorithms.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Hybrid Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Memetic Algorithms
;
Soft Computing
;
Swarm/Collective Intelligence
Abstract:
The goal of the clustering process is to find groups of similar patterns in multidimensional data. In this work,
the clustering problem is approached using cellular genetic algorithms. The population structure adopted in
the cellular genetic algorithm contributes to the population genetic diversity preventing the premature convergence
to local optima. The performance of the proposed algorithm is evaluated on 13 test databases. An
extension to the basic algorithm was also investigated to handle instances containing non-linearly separable
data. The algorithm is compared with nine non-evolutionary classification techniques from the literature, and
also compared with three nature inspired methodologies, namely Particle Swarm Optimization, Artificial Bee
Colony, and the Firefly Algorithm. The cellular genetic algorithm attains the best result on a test database. A
statistical ranking of the compared methods was made, and the proposed algorithm is ranked fifth overall.