Genetic and hybrid algorithms for graph coloring

A Hybrid Hopfield Network-Imperialist Competitive Algorithm for Solving the Satisfiability Problems

“…One way to overcome for this problem is Local search algorithms [11], [12], [13] and evolutionary algorithms [14]- [18].These include techniques Such as Simulated Annealing [19] The GSAT starts with a randomly generated truth assignment. It then changes the variable assignment that leads to the largest increase in the total number of clause satisfied.…”

Heuristic algorithms and learning techniques: applications to the graph coloring problem

“…DCNS-Distributed Coloration Neighborhood Search [Morgenstern, 1996], a large collection of algorithms and solving techniques, including population-based methods and the partial solution encoding; 1.2 Graph coloring: framework for experimental evaluation HGA-Hybrid Genetic Algorithms [Fleurent and Ferland, 1996b], a first hybridization of a coloring evolutionary algorithm with Tabu search, but with a (item) colororiented crossover; CISM-Crossover by Independent Sets and Mutation Search [Dorne and Hao, 1998a], an evolutionary algorithm using "Union of Independent Sets" crossover hybridized with a special Tabu algorithm employing certain random walk moves;…”

“…We are convinced that this is a fundamental issue for metaheuristics, i.e. a carefully designed evaluation function, [Chiarandini and Stützle, 2002;Paquete and Stützle, 2002], VNS , ALS [Devarenne et al, 2006] , PCOL [Blöchliger and Zufferey, 2008], VSS , DCNS [Morgenstern, 1996], HGA [Fleurent and Ferland, 1996b], HEA , AMCOL , MMT , MCOL . Notice that we indicate (in brackets) the results of the papers to appear in 2010, still in press at the defense of the thesis.…”

“…One should be aware that in the large graph literature coloring literature, there have been also tried other types of crossover operators, based on the color-oriented (array) encodings [Fleurent and Ferland, 1996b;, on unifying pairs of conflict-free sub-classes [Dorne and Hao, 1998a], on sexual reproduction using a splitting of the graph into two parts , distance-preserving crossovers , etc.…”

Parallel Simulated Annealing Algorithm for Graph Coloring Problem

“…Most algorithms designed for GCP are iterative heuristics [11], such as genetic algorithms [6], simulated annealing [7,8], tabu or local search techniques [9], minimizing selected cost functions. At the time of this writing, the only parallel metaheuristic for GCP is parallel genetic algorithm [10,[12][13][14][15].…”