TimGA: A genetic algorithm for drawing undirected graphs The problem of drawing graphs nicely contains several computationally intractable subproblems. Hence, it is natural to apply genetic algorithms to graph drawing. This paper introduces a Genetic Algorithm (TimGA) which nicely draws undirected graphs of moderate size. The aesthetic criteria used are the number of edge crossings, even distribution of nodes, and edge length deviation. Although TimGA usually works well, there are some unsolved problems related to the genetic crossover operation of graphs. Namely, our tests indicate that TimGA’s search is mainly guided by the mutation operations.