Searching databases using parallel genetic algorithms on a transputer computing surface
Future Generation Computer Systems - Special issue: MeikUS 92
Improvements to graph coloring register allocation
ACM Transactions on Programming Languages and Systems (TOPLAS)
Exact coloring of real-life graphs is easy
DAC '97 Proceedings of the 34th annual Design Automation Conference
New methods to color the vertices of a graph
Communications of the ACM
Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Future Generation Computer Systems - Special issue on bio-impaired solutions to parallel processing problems
Dynamic Flexible Constraint Satisfaction
Applied Intelligence
Register allocation and spilling via graph coloring
ACM SIGPLAN Notices - Best of PLDI 1979-1999
Future Generation Computer Systems - Special issue: Geocomputation
Frequency Allocation for WLANs Using Graph Colouring Techniques
WONS '05 Proceedings of the Second Annual Conference on Wireless On-demand Network Systems and Services
Efficient Hierarchical Parallel Genetic Algorithms using Grid computing
Future Generation Computer Systems
An adaptive memory algorithm for the k-coloring problem
Discrete Applied Mathematics
A hybrid immune algorithm with information gain for the graph coloring problem
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Scatter search technique for exam timetabling
Applied Intelligence
Conditional and composite temporal CSPs
Applied Intelligence
Performance evaluation of evolutionary heuristics in dynamic environments
Applied Intelligence
A graph coloring constructive hyper-heuristic for examination timetabling problems
Applied Intelligence
Graph 3-coloring with a hybrid self-adaptive evolutionary algorithm
Computational Optimization and Applications
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Graph Coloring Problems (GCPs) are constraint optimization problems with various applications including time tabling and frequency allocation. The GCP consists in finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. We propose a hierarchical approach based on Parallel Genetic Algorithms (PGAs) to solve the GCP. We call this new approach Hierarchical PGAs (HPGAs). In addition, we have developed a new operator designed to improve PGAs when solving constraint optimization problems in general and GCPs in particular. We call this new operator Genetic Modification (GM). Using the properties of variables and their relations, GM generates good individuals at each iteration and inserts them into the PGA population in the hope of reaching the optimal solution sooner. In the case of the GCP, the GM operator is based on a novel Variable Ordering Algorithm (VOA) that we propose. Together with the new crossover and the estimator of the initial solution we have developed, GM allows our solving approach to converge towards the optimal solution sooner than the well known methods for solving the GCP, even for hard instances. This was indeed clearly demonstrated by the experiments we conducted on the GCP instances taken from the well known DIMACS website.