Doctorado Santiago de Compostela

Programa de Doctorado Interuniversitario en Tecnologías de la Información
Curso: Técnicas de Computación Flexible
(Curso 2009-2010)


Francisco Herrera (Dpto. de Ciencias de la Computación e I.A.)


Material Complementario


  • Sesión 1: Genetic Algorithms. Basic concepts. 
  • Sesión 2: Genetic Algorithms. Advanced concepts. 
  • Sesión 3: Genetic Fuzzy Systems: State of the art and new trends. Part I. Introduction to GFSs. Part II. Tuning Methods. 
  • Sesión 4: Genetic Fuzzy Systems: State of the art and new trends. Part III. GFSs applications to HVAC problems. Part IV. Current trends and Prospects. 
    • Genetic Algorithms
      • D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, 1989.
      • Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs. Springer Verlag, 1996.
      • D.B. Fogel (Ed.) Evolutionary Computation. The Fossil Record. (Selected Readings on the History of Evolutionary Computation). IEEE Press, 1998.
      • A.E. Eiben, J.E. Smith. Introduction to Evolutionary Computation. Springer Verlag 2003. (Natural Computing Series)
      • D. Whitley "A Genetic Algorithm Tutorial". Statistics and Computing, 4, (1994), 65-85. 
      • F. Herrera, M. Lozano, J.L. Verdegay, Tackling Real-Coded Genetic Algorithms: Operators and tools for the Behaviour Analysis. Artificial Intelligence Review 12 (1998) 265-319. 
      • S. Ventura, C. Romero, A. Zafra, J.A. Delgado, C. Hervás-Martínez.JCLEC: A Java Framework for Evolutionary Computing. Soft Computing 12:4(2008) 381-392. 
      • D. Beasley, D. R. Bull, R. R. Martin. An Overview of Genetic Algorithms: Part 1, Fundamentals. University Computing 15:4 (1993) 170-181. 
      • D. Beasley, D. R. Bull, R. R. Martin. An Overview of Genetic Algorithms: Part 2, Research Topics. University Computing 15:4 (1993) 170-181. 
      • F. Herrera, M. Lozano, A.M. Sánchez. A Taxonomy for the Crossover Operator for Real-Coded Genetic Algorithms: An Experimental Study. International Journal of Intelligent Systems 18 (2003) 309-338, doi: 10.1002/int.10091. 
      • K. Deb. A population-based algorithm-generator for real-parameter optimization. Soft Computing 9:4 (2005) 236-253. 
      • J. Smith. On Replacement Strategies in Steady State Evolutionary Algorithms. Evolutionary Computation 15:1 (2007) 29-59. 
    • Niching Genetic Algorithms
      • B. Sareni, L. Krähenbühk, Fitness Sharing and Niching Methods Revisited. IEEE Transactions on Evolutionary Computation, Vol. 2, No. 3, Septiembre 1998, 97-106. 
      • Pétrowski, A. (1996). A clearing procedure as a niching method for genetic algorithms. In Proc. IEEE International conference on evolutionary computation. Japan. Pp. 798-803. 
      • Pérez, E., Herrera, F. and Hernández, C. (2003). Finding multiple solutions in job shop scheduling by niching genetic algorithms. Journal of Intelligent Manufacturing, (14) Pp. 323-341. 
    • Multiobjective Genetic Algorithms
      • C.A. Coello, D.A. Van Veldhuizen, G.B. Lamont, Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, 2002.
      • K. Deb, Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, 2001
      • E. Zitzler, L. Thiele. Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation 3:4 (1999) 257-217. 
      • E. Zitzler, K. Deb, L. Thiele. Comparison of Multiobjetive Evolutionary Algorithms: Empirical Results. Evolutionary Computation 8:2 (2000) 173-195. 
      • Eckart Zitzler, Marco Laumanns, Lothar Thiele: SPEA2: Improving the Strength Pareto Evolutionary Algorithm. Zürich, TIK Report Nr. 103, Computer Engineering and Networks Lab (TIK), Swiss Federal Institute of Technology (ETH) Zurich, May, 2001. 
      • K. Deb, A. Pratap, S. Agarwal and T. Meyarivan. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6:2 (2002) 182-197. 
      • C.A. Coello. Evolutionary Multiobjective Optimization: Current and Future Challenges. In J. Benitez, O. Cordon, F. Hoffmann, and R. Roy (Eds.), Advances in Soft Computing---Engineering, Design and Manufacturing. Springer-Verlag, September, 2003, pp. 243 - 256. 
      • E. Zitzler, L. Thiele, M. Laumanns, C.M. Fonseca, and V. Grunert da Fonseca. Performance Assessment of Multiobjective Optimizers: An Analysis and Review. IEEE Transactions on Evolutionary Computation 7:2, April, 2003, pp. 117 - 132. 
      • M. Laumanns, L. Thiele, K. Deb, and E. Zitzler. Combining Convergence and Diversity in Evolutionary Multi-objective Optimization. Evolutionary Computation 10:3, Fall, 2002, pp. 263 - 282. 
      • K. Deb, L. Thiele, M. Laumanns, and E. Zitzler. Scalable Test Problems for Evolutionary Multiobjective Optimization. In A. Abraham, L. Jain, and R. Goldberg (Eds.), Evolutionary Multiobjective Optimization. Theoretical Advances and Applications. Springer, USA, 2005, pp. 105 - 145. 
    • Memetic Algorithms
      • Recent Advances in Memetic Algorithms Studies in Fuzziness and Soft Computing, Vol. 166 Hart, William E.; Krasnogor, N.; Smith, J.E. (Eds.) 2005, X, 408 p., Hardcover ISBN: 3-540-22904-3
      • N. Krasnogor and J.E. Smith. A tutorial for competent memetic algorithms: model, taxonomy and design issues. IEEE Transactions on Evolutionary Computation 9(5):474- 488, 2005. 
      • Y.S. Ong and M.-H. Lim and N. Zhu and K.W. Wong. Classification of Adaptive Memetic Algorithms: a Comparative Study IEEE Transactions on System, Man. and Cybernetic 36:1, 141-152, 2006. 
      • P. Moscato, C. Cotta, Una Introducción a los Algoritmos Memeticos, Inteligencia Artificial. Revista Iberoamericana de IA, No. 19,2003, 131-148. 
      • Knowles, J.D.; Corne, D.W.; M-PAES: a memetic algorithm for multiobjective optimization Evolutionary Computation, 2000. Proceedings of the 2000 Congress on Volume 1, 16-19 July 2000 Page(s):325 - 332 vol.1 
      • Andrzej Jaszkiewicz Genetic Local Search for Multi-Objective Combinatorial Optimization European Journal of Operational Research 137, 2002, 50-71. 
      • Ishibuchi, H.; Yoshida, T.; Murata, T. Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling Evolutionary Computation, IEEE Transactions on 7:2 (2003), 204 - 223. 
    • Genetic Fuzzy Systems
      • O. Cordón, F. Gomide, F. Herrera, F. Hoffmann, L.Magdalena, Ten Years of Genetic Fuzzy Systems: Current Framework and New Trends. Fuzzy Sets and Systems 141:1 (2004) 5-31. 
      • F.Herrera, Genetic Fuzzy Systems: Status, Critical Considerations and Future Directions. International Journal of Computational IntelligenceResearch (IJCIR) 1:1 (2005) 59-67. 
      • F. Herrera, Genetic Fuzzy Systems: Taxonomy, Current Research Trends and Prospects. Evolutionary Intelligence 1 (2008) 27-46 doi: 10.1007/s12065-007-0001-5. 
    • Genetic Fuzzy Systems: Tuning Methods
      • M.J. Gacto, R. Alcalá, F. Herrera, Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems. Soft Computing 13:5 (2009) 419-436, doi:10.1007/s00500-008-0359-z 
      • R. Alcalá, M.J. Gacto, F. Herrera, J. Alcalá-Fdez, A Multi-objective Genetic Algorithm for Tuning and Rule Selection toObtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems.International Journal of Uncertainty, Fuzziness and Knowledge-BasedSystems, 15:5 (2007) 539, doi:10.1142/S0218488507004868. 
      • R. Alcalá, J. Alcalá-Fdez,F. Herrera, A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems andits Interaction with Rule Selection. IEEE Transactions onFuzzy Systems 15:4 (2007) 616-635, doi:10.1109/TFUZZ.2006.889880. 
      • R. Alcalá, J. Alcalá-Fdez,M.J. Gacto, F. Herrera, RuleBase Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5(2007) 401-419, doi:10.1007/s00500-006-0106-2. 
      • J. Casillas, O. Cordón, M.J.del Jesús, F.Herrera, Genetic Tuning of Fuzzy Rule Deep StructuresPreserving Interpretability and Its Interaction With Fuzzy Rule SetReduction. IEEE Trans. on Fuzzy Systems 13:1 (2005)13-29, doi:10.1109/TFUZZ.2004.839670.
      • F. Herrera, A Three-Stage Evolutionary Process for Learning Descriptive and Approximative Fuzzy Logic Controller Knowledge Bases from Examples.International Journal of Approximate Reasoning 17:4 (1997) 369-407. 
      • M. Lozano,J.L. Verdegay, Tuning Fuzzy Logic Controllers by GeneticAlgorithms. International Journal of ApproximateReasoning 12 (1995) 299-315, doi:10.1016/0888-613X(94)00033-Y. 
    • Genetic Fuzzy Systems: Application to HVAC Problems
      • R. Alcalá, J. Casillas, O. Cordón, A.González, F.Herrera, A Genetic Rule Weighting and Selection Process for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems. Engineering Applications of Artificial Intelligence 18:3 (2005)279-296, doi:10.1016/j.engappai.2004.09.007. 
      • R. Alcalá, J. Alcalá-Fdez,M.J. Gacto, F. Herrera, Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems. Applied Intelligence, 31:1 (2009) 15-30, doi:10.1007/s10489-007-0107-6.
      • R. Alcalá, J.M. Benítez, J. Casillas, O. Cordón, R.Pérez, Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms. Applied Intelligence 18:2 (2003) 155-177, doi:10.1023/A:1021986309149.