Improving the scalability of EA techniques: a case study in clustering

Authors:
Stefan R. Bach;A. Şima Uyar;Jürgen Branke
Affiliations:
University of Karlsruhe, Faculty of Informatics, Karlsruhe, Germany;Istanbul Technical University, Department of Computer Engineering, Maslak, Istanbul, Turkey;University of Warwick, Warwick Business School, Coventry, UK
Venue:
EA'09 Proceedings of the 9th international conference on Artificial evolution
Year:
2009

Citing 14
Cited 0

Genetic Algorithms and Grouping Problems

Genetic Algorithms and Grouping Problems
Further Experimentations on the Scalability of the GEMGA

PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Inoculation to Initialise Evolutionary Search

Selected Papers from AISB Workshop on Evolutionary Computing
A Two-Phase Genetic Algorithm for Large-Scale Bidline-Generation Problems at Delta Air Lines

Interfaces
A GA-Based Clustering Algorithm for Large Data Sets with Mixed Numeric and Categorical Values

ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
Convergence Time for the Linkage Learning Genetic Algorithm

Evolutionary Computation
An investigation of a hyperheuristic genetic algorithm applied to a trainer scheduling problem

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Sporadic model building for efficiency enhancement of the hierarchical BOA

Genetic Programming and Evolvable Machines
A survey of evolutionary algorithms for clustering

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

Soft Computing - A Fusion of Foundations, Methodologies and Applications
A two-level clustering method using linear linkage encoding

PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
On spanning-tree recombination in evolutionary large-scale networkproblems - application to electrical distribution planning

IEEE Transactions on Evolutionary Computation
Parallelism and evolutionary algorithms

IEEE Transactions on Evolutionary Computation
An Evolutionary Approach to Multiobjective Clustering

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper studies how evolutionary algorithms (EA) scale with growing genome size, when used for similarity-based clustering. A simple EA and EAs with problem-dependent knowledge are experimentally evaluated for clustering up to 100,000 objects. We find that EAs with problem-dependent crossover or hybridization scale near-linear in the size of the similarity matrix, while the simple EA, even with problem-dependent initialization, fails at moderately large genome sizes.