Heuristic algorithms and learning techniques: applications to the graph coloring problem
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Given an undirected graph G=(V ,E), the Graph Coloring Problem (GCP) consists in assigning a color to each vertex of the graph G in such a way that any two adjacent vertices are assigned different colors, and the number of different colors used is minimized. State-of-the-art algo
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
Constraint satisfaction problems (CSP) are widely studied in a large area of computer sciences, and many problems can be treated as CSPs. Furthermore, many solving methods exist such as complete methods, local search (for example tabu search) and genetic algorithms (GA) [4]. The
The Graph k-Colorability Problem (GCP) is a well known NP-hard problem which consist in finding the k minimum number of colors to paint the vertices of a graph in such a way that any two vertices joined by an edge have always different colors. Many years ago, Simulated Annealing
The graph coloring problem is a practical method of representing many real world problems including time scheduling, frequency assignment, register allocation, and circuit board testing. The most important fact that makes graph coloring exciting is that finding the minimum number
Given a graph G, an integer k, and a cost cuv associated with all pairs uv of non-adjacent vertices in G, the robust graph coloring problem is to assign a color in {1, · · · , k} to every vertex of G so that no edge has both endpoints with the same color, and the total cost of th