Algorithms for clustering data
Algorithms for clustering data
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Efficient coloring of a large spectrum of graphs
DAC '98 Proceedings of the 35th annual Design Automation Conference
New methods to color the vertices of a graph
Communications of the ACM
Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Graph Coloring with Adaptive Evolutionary Algorithms
Journal of Heuristics
Self-Adaptive Genetic Algorithm for Clustering
Journal of Heuristics
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid grouping genetic algorithm for the cell formation problem
Computers and Operations Research
A genetic rule-based data clustering toolkit
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Linear linkage encoding in grouping problems: applications on graph coloring and timetabling
PATAT'06 Proceedings of the 6th international conference on Practice and theory of automated timetabling VI
A two-level clustering method using linear linkage encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
New order-based crossovers for the graph coloring problem
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A two-leveled symbiotic evolutionary algorithm for clustering problems
Applied Intelligence
Solving Japanese nonograms by Taguchi-based genetic algorithm
Applied Intelligence
Cell assignment in hybrid CMOS/nanodevices architecture using Tabu Search
Applied Intelligence
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Linear Linkage Encoding (LLE) is a convenient representational scheme for Genetic Algorithms (GAs). LLE can be used when a GA is applied to a grouping problem and this representation does not suffer from the redundancy problem that exists in classical encoding schemes. LLE has been mainly used in data clustering. One-point crossover has been utilized in these applications. In fact, the standard recombination operators are not suitable to be used with LLE. These operators can easily disturb the building blocks and cannot fully exploit the power of the representation. In this study, a new crossover operator is introduced for LLE. The operator which is named as group-crossover is tested on the data clustering problem and a very significant performance increase is obtained compared to classical one-point and uniform crossover operations. Graph coloring is the second domain where the proposed framework is tested. This is a challenging combinatorial optimization problem for search methods and no significant success has been obtained on the problem with pure GA. The experimental results denote that GAs powered with LLE can provide satisfactory outcomes in this domain, too.