Computational Intelligence in Engineering pp 129-140 | Cite as
Comparative Investigation of Various Evolutionary and Memetic Algorithms
Chapter
Abstract
Optimization methods known from the literature include gradient techniques and evolutionary algorithms. The main idea of gradient methods is to calculate the gradient of the objective function at the actual point and then to step towards better values according to this value. Evolutionary algorithms imitate a simplified abstract model of evolution observed in nature. Memetic algorithms traditionally combine evolutionary and gradient techniques to exploit the advantages of both methods. Our current research aims to discover the properties, especially the efficiency (i.e. the speed of convergence) of particular evolutionary and memetic algorithms. For this purpose the techniques are compared on several numerical optimization benchmark functions and on machine learning problems.
Keywords
evolutionary algorithms memetic algorithms fuzzy rule-based learningPreview
Unable to display preview. Download preview PDF.
References
- 1.Balázs, K., Kóczy, L.T., Botzheim, J.: Comparison of Fuzzy Rule-based Learning and Inference Systems. In: Proceedings of the 9th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2008, Budapest, Hungary, pp. 61–75 (2008)Google Scholar
- 2.Balázs, K.: Comparative Analysis of Fuzzy Rule-based Learning and Inference Systems, Master’s Thesis, Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary, 97 p (2009)Google Scholar
- 3.Mamdani, E.H.: Application of Fuzzy Algorithms for Control of Simple Dynamic Plant. IEEE Proc. 121(12), 1585–1588 (1974)Google Scholar
- 4.Kóczy, L.T., Hirota, K.: Approximate Reasoning by Linear Rule Interpolation and General Approximation. Internat. J. Approx. Reason. 9, 197–225 (1993)MATHCrossRefMathSciNetGoogle Scholar
- 5.Tikk, D., Joó, I., Kóczy, L.T., Várlaki, P., Moser, B., Gedeon, T.D.: Stability of Interpolative Fuzzy KH-Controllers. Fuzzy Sets and Systems 125, 105–119 (2002)MATHCrossRefMathSciNetGoogle Scholar
- 6.Snyman, J.A.: Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-based Algorithms. Springer, New York (2005)MATHGoogle Scholar
- 7.Levenberg, K.: A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quart. Appl. Math. 2(2), 164–168 (1944)MATHMathSciNetGoogle Scholar
- 8.Marquardt, D.: An Algorithm for Least-Squares Estimation of Nonlinear Parameters. J. Soc. Indust. Appl. Math. 11(2), 431–441 (1963)MATHCrossRefMathSciNetGoogle Scholar
- 9.Holland, J.H.: Adaption in Natural and Artificial Systems. The MIT Press, Cambridge (1992)Google Scholar
- 10.Nawa, N.E., Furuhashi, T.: Fuzzy System Parameters Discovery by Bacterial Evolutionary Algorithm. IEEE Transactions on Fuzzy Systems 7(5), 608–616 (1999)CrossRefGoogle Scholar
- 11.Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks (ICNN 1995), Perth, WA, Australia, vol. 4, pp. 1942–1948 (1995)Google Scholar
- 12.Moscato, P.: On Evolution, Search, Optimization, Genetic Algorithms and Martial Arts: Towards Memetic Algorithms, Technical Report Caltech Concurrent Computation Program, Report. 826, California Institute of Technology, Pasadena, California, USA (1989)Google Scholar
- 13.Botzheim, J., Cabrita, C., Kóczy, L.T., Ruano, A.E.: Fuzzy Rule Extraction by Bacterial Memetic Algorithms. In: Proceedings of the 11th World Congress of International Fuzzy Systems Association, IFSA 2005, Beijing, China, pp. 1563–1568 (2005)Google Scholar
- 14.Balázs, K., Botzheim, J., Kóczy, L.T.: Comparative Analysis of Various Evolutionary and Memetic Algorithms. In: Proceedings of the 10th International Symposium of Hungarian Researchers on Computational Intelligence and Informatics, CINTI 2009, Budapest, Hungary, pp. 193–205 (2009)Google Scholar
- 15.Drobics, M., Botzheim, J., Kóczy, L.T.: Increasing Diagnostic Accuracy by Meta Optimization of Fuzzy Rule Bases. In: IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2007, London, UK, pp. 271–275 (2007)Google Scholar
- 16.Alpaydin, E.: Introduction to Machine Learning, 445 p. MIT Press, Cambridge (2004)Google Scholar
- 17.Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control, 316 p. Springer, New YorkGoogle Scholar
- 18.Ackley, D.: An Empirical Study of Bit Vector Function Optimization. Genetic Algorithms and Simulated Annealing, 170–215 (1987)Google Scholar
- 19.Keane, A.: Experiences with Optimizers in Structural Design. In: Parmee, I.C. (ed.) Proceedings of the 1st Conf. on Adaptive Computing in Engineering Design and Control, University of Plymouth, pp. 14–27. University of Plymouth, UK (1994)Google Scholar
- 20.Rastrigin, L.A.: Extremal Control Systems. Theoretical Foundations of Engineering Cybernetics Series. Moscow, Russian (1974)Google Scholar
- 21.Rosenbrock, H.H.: An Automatic Method for Finding the Greatest or Least Value of a Function. Computer Journal (3), 175–184 (1960)Google Scholar
- 22.Schwefel, H.P.: Numerical Optimization of Computer Models. John Wiley & Sons, Chichester (1981); english translation of Numerische Optimierung von Computer-modellen mittels der Evolutionsstrategie (1977)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2010