1. J.E. Baker, Reducing bias and inefficiency in the selection algorithm, in: J.J. Grefenstette (Ed.), Genetic Algorithms and Their Applications, Proceedings of the 2nd International Conference on Genetic Algorithms, Lawrence Erlbaum, London, 1987.
2. A framework for defining and learning fuzzy behaviors for autonomous mobile robots;Barberá;Int. J. Intell. Syst.,2002
3. T. Bäck, Evolutionary Algorithms in Theory and Practice, Oxford University Press, New York, 1996.
4. G. Dozier, Steady-state evolutionary path-planning, adaptive replacement, and hyper-diversity, in: M. Schoenauer, et al. (Eds.), Parallel Problem Solving from Nature, Proceedings of the 6th International Conference/PPNS VI, Lecture Notes in Computer Science, vol. 1917, Springer, Berlin, 2000, pp. 561–570.
5. M. Gemeinder, M. Gerke, GA based search for paths with minimum energy consumption for mobile robot systems, in: B. Reusch (Ed.), Computational Intelligence, Theory and Applications, Proceedings of the 7th Fuzzy Days, Lecture Notes in Computer Science, vol. 2206, Springer, Berlin, 2001, pp. 599–607.