A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
TANGO: A framework and system for algorithm animation
TANGO: A framework and system for algorithm animation
The “molecular” traveling salesman
Biological Cybernetics
Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
Genetic Algorithms for the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Paper: The parallel genetic algorithm as function optimizer
Parallel Computing
FOM: a framework for metaheuristic optimization
ICCS'03 Proceedings of the 2003 international conference on Computational science
The EvA2 optimization framework
LION'10 Proceedings of the 4th international conference on Learning and intelligent optimization
GAVEL - a new tool for genetic algorithm visualization
IEEE Transactions on Evolutionary Computation
An educational genetic algorithms learning tool
IEEE Transactions on Education
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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.