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Manuel Lozano: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Francisco Herrera, Manuel Lozano, E. Pérez, A. M. Sánchez, Pedro Villar
    Multiple Crossover per Couple with Selection of the Two Best Offspring: An Experimental Study with the BLX-alpha Crossover Operator for Real-Coded Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    IBERAMIA, 2002, pp:392-401 [Conf]
  2. Oscar Cordón, Francisco Herrera, Manuel Lozano
    A Three-Stage Method for Designing Genetic Fuzzy Systems by Learning from Examples. [Citation Graph (0, 0)][DBLP]
    PPSN, 1996, pp:720-729 [Conf]
  3. Carlos García-Martínez, Manuel Lozano, Daniel Molina
    A Local Genetic Algorithm for Binary-Coded Problems. [Citation Graph (0, 0)][DBLP]
    PPSN, 2006, pp:192-201 [Conf]
  4. Francisco Herrera, Manuel Lozano
    Adaptic Control of the Mutation Probability by Fuzzy Logic Controllers. [Citation Graph (0, 0)][DBLP]
    PPSN, 2000, pp:335-344 [Conf]
  5. Francisco Herrera, Manuel Lozano
    Heuristic Crossovers for Real-Coded Genetic Algorithms Based on Fuzzy Connectives. [Citation Graph (0, 0)][DBLP]
    PPSN, 1996, pp:336-345 [Conf]
  6. Francisco Herrera, Manuel Lozano, Claudio Moraga
    Hybrid Distributed Real-Coded Genetic Algorithms. [Citation Graph (0, 0)][DBLP]
    PPSN, 1998, pp:603-612 [Conf]
  7. Oscar Cordón, María José del Jesús, Francisco Herrera, Manuel Lozano
    Modelado cualitativo utilizando una metodología evolutiva de aprendizaje iterativo de bases de reglas difusas. [Citation Graph (0, 0)][DBLP]
    Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 1998, v:5, n:, pp:56-61 [Journal]
  8. Francisco Herrera, Manuel Lozano, José L. Verdegay
    Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis. [Citation Graph (0, 0)][DBLP]
    Artif. Intell. Rev., 1998, v:12, n:4, pp:265-319 [Journal]
  9. Francisco Herrera, Manuel Lozano
    Two-Loop Real-Coded Genetic Algorithms with Adaptive Control of Mutation Step Sizes. [Citation Graph (0, 0)][DBLP]
    Appl. Intell., 2000, v:13, n:3, pp:187-204 [Journal]
  10. José Ramón Cano, Francisco Herrera, Manuel Lozano
    On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. [Citation Graph (0, 0)][DBLP]
    Appl. Soft Comput., 2006, v:6, n:3, pp:323-332 [Journal]
  11. José Ramón Cano, Francisco Herrera, Manuel Lozano
    Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability. [Citation Graph (0, 0)][DBLP]
    Data Knowl. Eng., 2007, v:60, n:1, pp:90-108 [Journal]
  12. Manuel Lozano, Francisco Herrera, Natalio Krasnogor, Daniel Molina
    Real-Coded Memetic Algorithms with Crossover Hill-Climbing. [Citation Graph (0, 0)][DBLP]
    Evolutionary Computation, 2004, v:12, n:3, pp:273-302 [Journal]
  13. Francisco Herrera, Manuel Lozano, José L. Verdegay
    Tuning fuzzy logic controllers by genetic algorithms. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1995, v:12, n:3-4, pp:299-315 [Journal]
  14. Francisco Herrera, Manuel Lozano, A. M. Sánchez
    A taxonomy for the crossover operator for real-coded genetic algorithms: An experimental study. [Citation Graph (0, 0)][DBLP]
    Int. J. Intell. Syst., 2003, v:18, n:3, pp:309-338 [Journal]
  15. José Ramón Cano, Francisco Herrera, Manuel Lozano
    Stratification for scaling up evolutionary prototype selection. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition Letters, 2005, v:26, n:7, pp:953-963 [Journal]
  16. Francisco Herrera, Manuel Lozano
    Fuzzy adaptive genetic algorithms: design, taxonomy, and future directions. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2003, v:7, n:8, pp:545-562 [Journal]
  17. Francisco Herrera, Manuel Lozano
    Editorial Real coded genetic algorithms. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2005, v:9, n:4, pp:223-224 [Journal]
  18. Francisco Herrera, Manuel Lozano, A. M. Sánchez
    Hybrid crossover operators for real-coded genetic algorithms: an experimental study. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2005, v:9, n:4, pp:280-298 [Journal]
  19. José Ramón Cano, Francisco Herrera, Manuel Lozano
    Using evolutionary algorithms as instance selection for data reduction in KDD: an experimental study. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2003, v:7, n:6, pp:561-575 [Journal]
  20. Francisco Herrera, Manuel Lozano
    Gradual distributed real-coded genetic algorithms. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2000, v:4, n:1, pp:43-63 [Journal]
  21. Francisco Herrera, Manuel Lozano
    Adaptive genetic operators based on coevolution with fuzzy behaviors. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Evolutionary Computation, 2001, v:5, n:2, pp:149-165 [Journal]
  22. Francisco Herrera, Manuel Lozano, Daniel Molina
    Continuous scatter search: An analysis of the integration of some combination methods and improvement strategies. [Citation Graph (0, 0)][DBLP]
    European Journal of Operational Research, 2006, v:169, n:2, pp:450-476 [Journal]
  23. Carlos García-Martínez, Manuel Lozano
    Hybrid real-coded genetic algorithms with female and male differentiation. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:896-903 [Conf]
  24. Daniel Molina, Francisco Herrera, Manuel Lozano
    Adaptive local search parameters for real-coded memetic algorithms. [Citation Graph (0, 0)][DBLP]
    Congress on Evolutionary Computation, 2005, pp:888-895 [Conf]

  25. A memetic algorithm using local search chaining for black-box optimization benchmarking 2009 for noisy functions. [Citation Graph (, )][DBLP]


  26. A memetic algorithm using local search chaining forblack-box optimization benchmarking 2009 for noise free functions. [Citation Graph (, )][DBLP]


  27. A continuous variable neighbourhood search based on specialised EAs: application to the noiseless BBO-benchmark 2009. [Citation Graph (, )][DBLP]


  28. A continuous variable neighbourhood search based on specialised eas: application to the noisy BBO-benchmark 2009 testbed. [Citation Graph (, )][DBLP]


  29. P system model optimisation by means of evolutionary based search algorithms. [Citation Graph (, )][DBLP]


  30. Evolutionary symbolic discovery for bioinformatics, systems and synthetic biology. [Citation Graph (, )][DBLP]


  31. Memetic Algorithm for Intense Local Search Methods Using Local Search Chains. [Citation Graph (, )][DBLP]


  32. An Evolutionary ILS-Perturbation Technique. [Citation Graph (, )][DBLP]


  33. Saw-Tooth Algorithm Guided by the Variance of Best Individual Distributions for Designing Evolutionary Neural Networks. [Citation Graph (, )][DBLP]


  34. Memetic Algorithm with Local Search Chaining for Continuous Optimization Problems: A Scalability Test. [Citation Graph (, )][DBLP]


  35. Continuous Variable Neighbourhood Search Algorithm Based on Evolutionary Metaheuristic Components: A Scalability Test. [Citation Graph (, )][DBLP]


  36. Memetic algorithm with Local search chaining for large scale continuous optimization problems. [Citation Graph (, )][DBLP]


  37. Simulated annealing based on local genetic search. [Citation Graph (, )][DBLP]


  38. Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems. [Citation Graph (, )][DBLP]


  39. Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: Overview and progress report. [Citation Graph (, )][DBLP]


  40. Memetic Algorithms for Continuous Optimisation Based on Local Search Chains. [Citation Graph (, )][DBLP]


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