GraphEA: A 3D Educational Tool for Genetic Algorithms



During the last decades Genetic Algorithms (GAs) have proved to be a powerful technique for solving difficult problems. Consequently, GA courses are becoming increasingly common in universities. The laboratorial classes of such courses are crucial for students to consolidate and apply the concepts learned in theoretical classes. However, it is required a lot of programming effort and sometimes students tend to have difficulties on this part, either because the number of different GA variants they have to implement or even because the lack of programming skills. To overcome this problem we present a new educational tool for GAs called GraphEA. This tool aims to help students to learn GAs without the need of programming effort, offering novel features like the 3D visualization of the chromosome formation process and the online modification of problem data. In this paper we demonstrate three well-known optimization problems implemented on the tool, namely the Knapsack Problem, the Traveling Salesman Problem, and the Function Optimization Problem.


Genetic algorithms, educational and visualization tools, evolutionary computation


Proceedings of the 2013 Genetic and Evolutionary Computation Conference (GECCO 2013), Christian Blum (Ed.), pp. 1293-1300, Amsterdam, The Netherlands, 06-10, ACM, New York, NY, USA, 2013. 2013

Cited by

Year 2015 : 1 citations

 Cruz, A., Machado, P., Assunção, F., & Leitão, A. (2015, July). ELICIT: Evolutionary Computation Visualization. In Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference (pp. 949-956). ACM.

Year 2014 : 1 citations

 Koon, E. (2014). Assembling 3D Objects with Artificial Spatial Intelligence.