Together with Matlab and Simulink, the GA Toolbox described in this paper presents a familiar and unified environment for the control engineer to experiment with and apply GAs to tasks in control systems engineering. Whilst the GA Toolbox was developed with the emphasis on control engineering applications, it should prove equally useful in the general field of GAs, particularly given the range of domain-specific toolboxes available for the Matlab package.
[1] Ackermann, J.: Robuste Regelung. Berlin, Heidelberg, New York: Springer, 1993
[2] Baker, J. E.: Adaptive Selection Methods for Genetic Algorithms. Proceedings of an International Conference on Genetic Algorithms and their Application, pp. 101-111, Hillsdale, New Jersey, USA: Lawrence Erlbaum Associates, 1985
[3] Baker, J. E.: Reducing Bias and Inefficiency in the Selection Algorithm. Proceedings of the Second International Conference on Genetic Algorithms and their Application, pp. 14-21, Hillsdale, New Jersey, USA: Lawrence Erlbaum Associates, 1987
[4] Chipperfield, A. J., Fleming, P. J. and Pohlheim, H.: A Genetic Algorithm Toolbox for MATLAB. Proc. Int. Conf. Sys. Engineering, Coventry, UK, 6-8 Sept., pp. 200-207, 1994
[5] Fonseca, C. M. and Fleming P. J.: Genetic Algorithms for Multiple Objective Optimization: Formulation, Discussion and Generalization. Proceedings of the Fifth International Conference on Genetic Algorithms and their Application, pp. 416-423, San Mateo, California, USA: Morgan Kaufmann Publishers, 1993
[6] Goldberg, D. E.: Genetic Algorithms in Search, Optimization, and Machine Learning. Reading, Mass.: Addison-Wesley, 1989
[7] Haas, R. and Hunt, K. J.: Genetic based optimisation of a fuzzy-neural vehicle controller. Proc. Fuzzy Systems Conf., Munich, 1994
[8] Hoffmeister, F. and Bäck, T.: Genetic Algorithms and Evolutionary Strategies: Similarities and Differences. Proceedings of Parallel Problems Solving from Nature, pp. 455-469, volume 496 of Lecture Notes in Computer Science, Berlin, Heidelberg, New York: Springer, 1991
[9] Mecklenburg, K., Hrycej, T., Franke, U. and Fritz, H.: Neural control of autonomous vehicle. in IEEE Vehicular Technology Conference, Denver, 1992
[10] Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Berlin, Heidelberg, New York: Springer, 1992
[11] Mitschke, M.: Dynamik der Kraftfahrzeuge: Band C, Fahrverhalten. Berlin, Heidelberg, New York: Springer, 1990
[12] Mühlenbein, H. and Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm: I. Continuous Parameter Optimization. Evolutionary Computation, 1 (1), pp.25-49, 1993
[13] Mühlenbein, H., Schomisch, M. and Born, J.: The parallel genetic algorithm as function optimizer. Parallel Computing, 17, pp.619-632, 1991
[14] Mühlenbein, H.: The Breeder Genetic Algorithm - a provable optimal search algorithm and its application. Colloquium on Applications of Genetic Algorithms, IEE 94/067, London, 1994
[15] Pohlheim, H.: Ein genetischer Algorithmus mit Mehrfachpopulationen zur Numerischen Optimierung, to appear in at-Automatisierungstechnik 3 (1995), Berlin, 1995
[16] Whitley, D.: The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best. Proceedings of the Third International Conference on Genetic Algorithms and their Application, pp. 116-121, San Mateo, California, USA: Morgan Kaufmann Publishers, 1989