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)
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
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
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
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
An approach to solve winner determination in combinatorial reverse auctions using genetic algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Graph coloring problems (GCPs) are constraint optimization problems with various applications including scheduling, 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 parallel approach based on Hierarchical Parallel Genetic Algorithms (HPGAs) to solve the GCP. We also propose a new extension to PGA, that is Genetic Modification (GM) operator designed for solving constraint optimization problems by taking advantage of the properties between variables and their relations. Our proposed GM for solving the GCP is based on a novel Variable Ordering Algorithm (VOA). In order to evaluate the performance of our new approach, we have conducted several experiments on GCP instances taken from the well known DIMACS website. The results show that the proposed approach has a high performance in time and quality of the solution returned in solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.