Using Differential Evolution for the Graph Coloring (original) (raw)

2012

Differential evolution was developed for reliable and versatile function optimization. It has also become interesting for other domains because of its ease to use. In this paper, we posed the question of whether differential evolution can also be used by solving of the combinatorial optimization problems, and in particular, for the graph coloring problem. Therefore, a hybrid self-adaptive differential evolution algorithm for graph coloring was proposed that is comparable with the best heuristics for graph coloring today, i.e. Tabucol of Hertz and de Werra and the hybrid evolutionary algorithm of Galinier and Hao. We have focused on the graph 3-coloring. Therefore, the evolutionary algorithm with method SAW of Eiben et al., which achieved excellent results for this kind of graphs, was also incorporated into this study. The extensive experiments show that the differential evolution could become a competitive tool for the solving of graph coloring problem in the future.

Genetic Algorithm Applied to the Graph Coloring Problem

2012

Abstract In this paper we present a hybrid technique that applies a genetic algorithm followed by wisdom of artificial crowds approach to solving the graph-coloring problem. The genetic algorithm described here utilizes more than one parent selection and mutation methods depending on the state of fitness of its best solution. This results in shifting the solution to the global optimum more quickly than using a single parent selection or mutation method.

An Approach to Solve the Graph Coloring Problem by Genetic Algorithms

International Journal of Advance Research and Innovative Ideas in Education, 2018

Let G = (V,E) an undirected graph, V corresponds to the set of vertices and E corresponds to the set of edges, we focus on the graph coloring problem (GCP), which consist to associate a color to each vertex so that no two adjacent vertices possess the same color. In this paper we propose a new genetic algorithm based on heuristic to approximate values of v(G) for GCP which achieves highly competitive results.

A Comparative Study of Various Strategies in Differential Evolution

This paper presents a comparison of various strategies of differential evolution. Differential evolution (DE) is a simple and powerful optimization method, which is mainly applied to numerical optimization and many other problems (for example: neural network train, filter design or image analysis). The comparison of various modifications (named strategies) of DE algorithm allows to choose the algorithm version, which is best adjusted to desirable requirements. Three parameters are tested: speed, accuracy and completeness. The first section presents general optimization problem, and says little about methods used to function optimization. The next section describes a method of differential evolution – basic algorithm is presented. Two different crossover methods, process of initial population creation and basic mutation schema are described. The third section contains chosen by authors different mutation strategies (called DE strategies), and their variations. Every strategy is descr...

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