Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge, Workshop, October 11-13, 1993
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Memetic Algorithm for University Exam Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Breaking Instance-Independent Symmetries in Exact Graph Coloring
Proceedings of the conference on Design, automation and test in Europe - Volume 1
A Developmental Approach to the Uncapacitated Examination Timetabling Problem
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
A Grouping Genetic Algorithm Using Linear Linkage Encoding for Bin Packing
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Multi-objective Genetic Algorithms for grouping problems
Applied Intelligence
Scatter search technique for exam timetabling
Applied Intelligence
Information Sciences: an International Journal
Genetic programming for auction based scheduling
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
A particle swarm optimizer for grouping problems
Information Sciences: an International Journal
Evolutionary algorithms using cluster patterns for timetabling
Intelligent Decision Technologies
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Linear Linkage Encoding (LLE) is a recently proposed representation scheme for evolutionary algorithms. This representation has been used only in data clustering. However, it is also suitable for grouping problems. In this paper, we investigate LLE on two grouping problems; graph coloring and exam timetabling. Two crossover operators suitable for LLE are proposed and compared to the existing ones. Initial results show that LLE is a viable candidate for grouping problems whenever appropriate genetic operators are used.