ALAR

The Artificial Life and Adaptive Robotics Laboratory

 

Selected Papers Cited Our Work

If you know of any paper cited our work that is not listed here, please email it to h.abbass @ adfa.edu.au

  1. Alander, J.T., Indexed bibliography of genetic programming. Report Series no, 1995: p. 94-1.
  2. Alatas, B. and E. Akin, FCACO: Fuzzy Classification Rules Mining Algorithm with Ant Colony Optimization. Lecture Notes In Computer Science, 2005. 3612: p. 787.
  3. Amarteifio, S., Interpreting a Genotype-Phenotype Map with Rich Representations in XMLGE.
  4. Babu, B.V., P.G. Chakole, and J.H.S. Mubeen, Multiobjective differential evolution (MODE) for optimization of adiabatic styrene reactor. Chemical Engineering Science, 2005. 60(17): p. 4822-4837.
  5. Babu, B.V. and M.M.L. Jehan, Differential evolution for multi-objective optimization. Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, 2003. 4.
  6. Babu, B.V., J.H.S. Mubeen, and P.G. Chakole, Multi-objective Optimization Using Differential Evolution. TechGenesis-The Journal of Information Technology, 2005. 2: p. 4-12.
  7. Banos, G., et al., Quality control of national genetic evaluation results using data mining techniques; a progress report. Proc. 2003 Interbull Annual Meeting, 2003. 31: p. 8-15.
  8. Bate, A., The use of a Bayesian confidence propagation neural network in pharmacovigilance: Umeå University.
  9. Bath, P.A., Data mining in health and medical information. Annual Review of Information Science and Technology, 2004. 38: p. 331-369.
  10. Benatchba, K., L. Admane, and M. Koudil, Using Bees to Solve a Data-Mining Problem Expressed as a Max-Sat One. Lecture notes in computer science: p. 212-220.
  11. Bergey, P.K. and C. Ragsdale, Modified differential evolution: a greedy random strategy for genetic recombination. Omega-International Journal of Management Science, 2005. 33(3): p. 255-265.
  12. Bloehdorn, S., P. Cimiano, and A. Hotho. Learning Ontologies to Improve Text Clustering and Classification. in Proc of GFKL. 2005.
  13. Bloehdorn, S., et al., An Ontology-based Framework for Text Mining. Journal for Computational Linguistics and Language Technologie, 2005. 20(1): p. 87-112.
  14. Bloehdorn, S. and A. Hotho, Boosting for Text Classification with Semantic Features. Proc. of the Mining for and from the Semantic Web Workshop at KDD, 2004. 2004.
  15. Bosman, P.A.N. and E.D. de Jong, Grammar Transformations in an EDA for Genetic Programming. GECCO 2004 Workshop Proceedings, Seattle, Washington, USA, 2004: p. 26-30.
  16. Bosman, P.A.N. and E.D. de Jong, Learning Probabilistic Tree Grammars for Genetic Programming. Proceedings of the 8th International Conference on Parallel Problem Solving from Nature PPSN-04, 2004.
  17. Brown, G., Diversity in Neural Network Ensembles. School of Computer Science, The University of Birmingham, UK, 2004.
  18. Brown, G., et al., Diversity creation methods: a survey and categorisation. Information Fusion, 2005. 6(1): p. 5-20.
  19. Brown, G., J.L. Wyatt, and P. Tino, Managing diversity in regression ensembles. Journal of Machine Learning Research, 2005. 6: p. 1621-1650.
  20. Büche, D., Multi-Objective Evolutionary Optimization of Gas Turbine Components. 2003, Swiss Federal Institute of Technology.
  21. Buche, D., S. Muller, and P. Koumoutsakos, Self-Adaptation for Multi-objective Evolutionary Algorithms. Proceedings of the Second International Conference on Evolutionary Multi-Criterion Optimization (EMO 2003), 2003. 2632: p. 267–281.
  22. Buontempo, F.V., et al., Genetic programming for the induction of decision trees to model ecotoxicity data. Journal of Chemical Information and Modeling, 2005. 45(4): p. 904-912.
  23. Burke, E., S. Gustafson, and G. Kendall, A survey and analysis of diversity measures in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference, 2002: p. 716–723.
  24. Burke, E., et al., Advanced population diversity measures in genetic programming. Parallel Problem Solving from Nature, 2002. 2439: p. 341–350.
  25. Burke, E.K., S. Gustafson, and G. Kendall, Diversity in genetic programming: an analysis of measures and correlation with fitness. Evolutionary Computation, IEEE Transactions on, 2004. 8(1): p. 47-62.
  26. Burtsev, M.S., Tracking the Trajectories of Evolution. Artificial Life, 2004. 10(4-Fall).
  27. Cannataro, M., et al., Computational Intelligence.
  28. Chandra, A. and X. Yao, DIVACE: Diverse and Accurate Ensemble Learning Algorithm. Lecture Notes in Computer Science 3177: Intelligent Data Engineering and Automated Learning-IDEAL, 2004: p. 619-625.
  29. Chandra, A. and X. Yao, Evolutionary framework for the construction of diverse hybrid ensembles. Proc. of the 13th European Symposium on Artificial Neural Networks-ESANN 2005, 2005: p. 253-258.
  30. Chang, H.S., Converging Marriage in Honey-Bees Optimization and Application to Stochastic Dynamic Programming. Journal of Global Optimization, 2006. 35(3): p. 423-441.
  31. Chen, T.C. and T.C. Hsu, A GAs based approach for mining breast cancer pattern. Expert Systems With Applications, 2006. 30(4): p. 674-681.
  32. Cleary, R., Extending Grammatical Evolution with Attribute Grammars: An Application to Knapsack Problems. 2005, University of Limerick
  33. Corne, D.W., et al., The Good of the Many Outweighs the Good of the One: Evolutionary Multi-Objective Optimization. Connections. The Newsletter of the IEEE Neural Networks Society, 2003. 1(1): p. 9–13.
  34. De, D., et al., A Fuzzy Logic Controller Based Dynamic Routing Algorithm with SPDE based Differential Evolution Approach.
  35. de Torneado, P., Optimización Multiobjetivos del.
  36. Deb, K., Softening the Structural Difficulty in Genetic Programming with TAG-Based Representation and Insertion/Deletion Operators. GECCO 2004, LNCS 3103, 2004: p. 605-616.
  37. Deb, K. and S. Jain, Running performance metrics for evolutionary multi-objective optimization. KanGAL Report No, 2002. 2002004.
  38. del Amo, I.J.G., et al., Data Mining with Scatter Search, in Computer Aided Systems Theory - Eurocast 2005. 2005. p. 199-204.
  39. Delen, D., G. Walker, and A. Kadam, Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine, 2005. 34(2): p. 113–127.
  40. Diplaris, S., et al., A decision-tree-based alarming system for the validation of national genetic evaluations. Computers and Electronics in Agriculture, 2006. 52(1-2): p. 21-35.
  41. Dounias, G., Hybrid Computational Intelligence in Medicine.
  42. Fan, H.Y., J. Lampinen, and Y. Levy, An easy-to-implement differential evolution approach for multi-objective optimizations. Engineering Computations, 2006. 23(2): p. 124-138.
  43. Fieldsend, J.E. and S. Singh, Pareto evolutionary neural networks. Neural Networks, IEEE Transactions on, 2005. 16(2): p. 338-354.
  44. Ga, D.K., A Simple and Global Optimization Algorithm for Engineering Problems: Differential Evolution Algorithm. Turk J Elec Engin, 2004. 12(1).
  45. Gen, M. and L. Lin, Multiobjective hybrid genetic algorithm for bicriteria network design problem. The 8th Asia Pacific Symposium on Intelligent and Evolutionary Systems, December, 2004.
  46. Gepperth, A. and S. Roth, Applications of multi-objective structure optimization. Neurocomputing, 2006. 69(7-9): p. 701-713.
  47. Gepperth, A.R.T., Neural learning methods for visual object detection.
  48. Goh, T.T., Data mining: A heuristic approach. Online Information Review, 2003. 27(5): p. 364-365.
  49. Graves, R.J., et al., Advanced Research in Scalable Enterprise Systems: Multi-Objective Optimization.
  50. Gustafson, S. and E.K. Burke, A Niche for Parallel Island Models: Outliers and Local Search. Parallel Processing, 2005. ICPP 2005 Workshops. International Conference Workshops on, 2005: p. 612-619.
  51. Gustafson, S.M., An Analysis of Diversity in Genetic Programming. 2004, University of Nottingham.
  52. Güven, A. and S. Kara, Diagnosis of the macular diseases from pattern electroretinography signals using artificial neural networks. Expert Systems With Applications, 2006. 30(2): p. 361-366.
  53. Haddad, O.B., A. Afshar, and M.A. Marino, Honey-bees mating optimization (HBMO) algorithm: A new heuristic approach for water resources optimization. Water Resources Management, 2006. 20(5): p. 661-680.
  54. Handl, J., D.B. Kell, and J. Knowles, Multiobjective optimization in bioinformatics and computational biology.
  55. Harris, G. and X. Yao, Diversity Creation Methods: A Survey and Categorisation.
  56. He, K., et al., Design Methodology of Networked Software Evolution Growth Based on Software Patterns. Journal of Systems Science and Complexity, 2006. 19(2): p. 157-181.
  57. Hernández-Díaz, A.G., et al., A new proposal for multi-objective optimization using differential evolution and rough sets theory. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 675-682.
  58. Higgins, A.J., et al., Developing and implementing optimised sugarcane harvest schedules through participatory research. Australian Journal of Agricultural Research, 2004. 55: p. 297-306.
  59. Hsu, F.C., et al., Technology and knowledge document cluster analysis for enterprise R&D strategic planning. International Journal of Technology Management, 2006. 36(4): p. 336-353.
  60. Hu, S., H. Huang, and D. Czarkowski, Hybrid trigonometric differential evolution for optimizing harmonic distribution. IEEE international symposium on circuits and systems, 2005. 2: p. 1306.
  61. Huband, S., et al., An evolution strategy with probabilistic mutation for multi-objective optimisation. Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, 2003. 4.
  62. Hurst, J. and L. Bull, A neural learning classifier system with self-adaptive constructivism for mobile robot control. Artificial Life, 2006. 12(3): p. 353-380.
  63. Igel, C., Multi-objective Model Selection for Support Vector Machines. Proceedings of the Third International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005), 2005. 3410.
  64. Igel, C. and B. Sendhoff, Synergies between Evolutionary and Neural Computation. 13th European Symposium on Artificial Neural Networks (ESANN 2005): p. 241–252.
  65. Iorio, A.W. and X. Li, Incorporating directional information within a differential evolution algorithm for multi-objective optimization. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 691-698.
  66. Iorio, A.W. and X. Li, Solving rotated multi-objective optimization problems using differential evolution. AI 2004: Advances in Artificial Intelligence: p. 861-872.
  67. Ishibuchi, H. and Y. Nojima, Accuracy-Complexity Tradeoff Analysis by Multiobjective Rule Selection.
  68. Ishibuchi, H. and Y. Nojima, Performance Evaluation of Evolutionary Multiobjective Approaches to the Design of Fuzzy Rule-Based Ensemble Classifiers. Hybrid Intelligent Systems, 2005. Fifth International Conference on, 2005: p. 271-276.
  69. Janikow, C.Z., Adapting Representation in Genetic Programming. Proceedings of Genetic and Evolutionary Computation Conference: GECCO, 2004: p. 507-518.
  70. Jensen, M.T., Helper-Objectives: Using Multi-Objective Evolutionary Algorithms for Single-Objective Optimisation. Journal of Mathematical Modelling and Algorithms, 2004. 3(4): p. 323-347.
  71. Jensen, M.T., Reducing the run-time complexity of multiobjective EAs: The NSGA-II and other algorithms. Evolutionary Computation, IEEE Transactions on, 2003. 7(5): p. 503-515.
  72. Jerez-Aragones, J.M., et al., A combined neural network and decision trees model for prognosis of breast cancer relapse. Artif Intell Med, 2003. 27(1): p. 45-63.
  73. Jiang, W., Y. Xu, and Y. Xu, A Novel Data Mining Method Based on Ant Colony Algorithm. LECTURE NOTES IN COMPUTER SCIENCE, 2005. 3584: p. 284.
  74. Jiang, W.J., Y.H. Xu, and Y.S. Xu, A Novel Data Mining Algorithm Based on Ant Colony System. Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on, 2005. 3.
  75. Jin, Y., T. Okabe, and B. Sendhoff, Neural network regularization and ensembling using multi-objective evolutionary algorithms. Evolutionary Computation, 2004. CEC2004. Congress on, 2004. 1.
  76. Jin, Y., B. Sendhoff, and E. Korner, Evolutionary multi-objective optimization for simultaneous generation of signal-type and symbol-type representations. Evolutionary Multi-Criterion Optimization. 2006. 752-766.
  77. Kacem, I., S. Hammadi, and P. Borne, Approach by localization and multiobjective evolutionaryoptimization for flexible job-shop scheduling problems. Systems, Man and Cybernetics, Part C, IEEE Transactions on, 2002. 32(1): p. 1-13.
  78. Kacem, I., S. Hammadi, and P. Borne, Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic. Mathematics and Computers in Simulation, 2002. 60(3): p. 245-276.
  79. Keijzer, M., Genetic Transposition in Tree-Adjoining Grammar Guided Genetic Programming: The Duplication Operator. EuroGP 2005, LNCS 3447, 2005: p. 108-119.
  80. Keijzer, M., Toward an Alternative Comparison between Different Genetic Programming Systems. EuroGP 2004, LNCS 3003, 2004: p. 67–77.
  81. Kim, K.J. and S.B. Cho, A comprehensive overview of the applications of artificial life. Artificial Life, 2006. 12(1): p. 153-182.
  82. Knowles, J. and D. Corne, Memetic Algorithms for Multiobjective Optimization: Issues, Methods and Prospects. Recent Advances in Memetic Algorithms. 166: p. 313–352.
  83. Konukman, A.E.S. and U. Akman, Flexibility and operability analysis of a HEN-integrated natural gas expander plant. Chemical Engineering Science, 2005. 60(24): p. 7057-7074.
  84. Kostoff, R.N. and J.A. Block, Factor Matrix Text Filtering and Clustering. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, 2005. 56(9): p. 946-968.
  85. Krkoska, M., Feature Weighting for Ontology Extraction. 2003, Universit¨at Karlsruhe.
  86. Kukkonen, S. and J. Lampinen, An extension of generalized differential evolution for multi-objective optimization with constraints, in Parallel Problem Solving from Nature - Ppsn Viii. 2004. p. 752-761.
  87. Laurent, J. and M.T. Druot, Evolution of a neural network for the control of a flapping-wing animat.
  88. Li, G., K.H. Lee, and K.S. Leung, Evolve Schema Directly Using Instruction Matrix Based Genetic Programming. Lecture notes in computer science: p. 271-280.
  89. Li, S. and Y. Liu, Intelligent Forecast Procedures for Slope Stability with Evolutionary Artificial Neural Network. LECTURE NOTES IN COMPUTER SCIENCE, 2004: p. 792-798.
  90. Liu, J. and J. Lampinen, A Fuzzy Adaptive Differential Evolution Algorithm. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2005. 9(6): p. 448-462.
  91. Looks, M., Learning with Semantic Spaces: From Parameter Tuning to Discovery.
  92. Looks, M., B. Goertzel, and C. Pennachin, Learning computer programs with the bayesian optimization algorithm. 2005: ACM Press New York, NY, USA.
  93. Luerssen, M.H., Graph grammar encoding and evolution of automata networks. Proceedings of the Twenty-eighth Australasian conference on Computer Science-Volume 38, 2005: p. 229-238.
  94. Luerssen, M.H. and D.M.W. Powers, Graph composition in a graph grammar-based method for automata network evolution. Evolutionary Computation, 2005. The 2005 IEEE Congress on, 2005. 2.
  95. Madavan, N.K., On Improving Efficiency of Differential Evolution for Aerodynamic Shape Optimization Applications. 10 th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2004.
  96. Mattfeldt, T., H.A. Kestler, and H.P. Sinn, Prediction of the axillary lymph node status in mammary cancer on the basis of clinicopathological data and flow cytometry. Medical and Biological Engineering and Computing, 2004. 42(6): p. 733-739.
  97. Melville, P. and R.J. Mooney, Creating diversity in ensembles using artificial data. Information Fusion, 2005. 6(1): p. 99-111.
  98. Mezura-Montes, E., J. Velázquez-Reyes, and C.A.C. Coello, A comparative study of differential evolution variants for global optimization. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 485-492.
  99. Min, Z., L. Xiang-guan, and L. Shi-hua, An Evolutionary Artificial Neural Networks Approach for BF Hot Metal Silicon Content Prediction. LECTURE NOTES IN COMPUTER SCIENCE, 2005. 3610: p. 374.
  100. Narukawa, K., Y. Nojima, and H. Ishibuchi, Modification of Evolutionary Multiobjective Optimization Algorithms for Multiobjective Design of Fuzzy Rule-Based Classification Systems. Fuzzy Systems, 2005. FUZZ'05. The 14th IEEE International Conference on, 2005: p. 809-814.
  101. Nattkemper, T.W., et al., Evaluation of radiological features for breast tumour classification in clinical screening with machine learning methods. Artificial Intelligence in Medicine, 2005. 34(2): p. 129-139.
  102. Nicosia, G., Multimodal Search with Immune Based Genetic Programming. ICARIS, LNCS 3239, 2004: p. 330–341.
  103. O’Neill, M. and A. Brabazon, mGGA: The meta-Grammar Genetic Algorithm. Proceedings of the 8th European Conference on Genetic Programming, 2005. 3447: p. 311–320.
  104. Oakes, M., Ant colony optimisation for stylometry: The federalist papers. Proceedings of the 5th International Conference on Recent Advances in Soft Computing: p. 86–91.
  105. Omran, M.G.H., A. Salman, and A.P. Engelbrecht, Self-adaptive differential evolution, in Computational Intelligence and Security, Pt 1, Proceedings. 2005. p. 192-199.
  106. Owais, S.S.J., P. Kromer, and V. Snasel, Evolutionary Learning of Boolean Queries by Genetic Programming. information retrieval. 3(2): p. 15.
  107. Parsopoulos, K.E., et al., Vector evaluated differential evolution for multiobjective optimization. Evolutionary Computation, 2004. CEC2004. Congress on, 2004. 1.
  108. Pulido, G.T. and C.A.C. Coello, The Micro Genetic Algorithm 2: Towards Online Adaptation in Evolutionary Multiobjective Optimization. Evolutionary Multi-Criterion Optimization. Second International Conference, EMO, 2003: p. 252–266.
  109. Purshouse, R.C., On the Evolutionary Optimisation of Many Objectives. 2003, University of Sheffield.
  110. Quintero, L.V.S., M. en Ciencias, and O. Computación, Un Algoritmo Basado en Evolución Diferencial para Resolver Problemas Multiobjetivo.
  111. Rai, M.M., Robust Optimal Design with Differential Evolution. AIAA Paper, 2004. 4588(10).
  112. Reyes-Sierra, M., et al., Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art.
  113. Robic, T. and B. Filipic, DEMO: Differential Evolution for Multiobjective Optimization. Proceedings of the Conference on Evolutionary Multiobjective Optimization, 2005.
  114. Roth, S., A. Gepperth, and C. Igel, Multi-objective neural network optimization for visual object detection.
  115. Santana-Quintero, L.V. and C.A.C. Coello, An Algorithm Based on Differential Evolution for Multiobjective Problems.
  116. Sardinas, R.Q., M.R. Santana, and E.A. Brindis, Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence, 2006. 19(2): p. 127-133.
  117. Sastry, K., C.F. Lima, and D.E. Goldberg, Evaluation relaxation using substructural information and linear estimation. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 419-426.
  118. Smaldon, J. and A.A. Freitas, A new version of the ant-miner algorithm discovering unordered rule sets. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 43-50.
  119. Sokolov, A., et al., Dynamic power minimization during combinational circuit testing as a traveling salesman problem. Evolutionary Computation, 2005. The 2005 IEEE Congress on, 2005. 2.
  120. Subbu, R., et al., Advanced Research in Scalable Enterprise Systems: Network-based Distributed Relational Decision Framework for Scalable Enterprise Systems-Phase II.
  121. Tan, K.C., et al., Evolving better population distribution and exploration in evolutionary multi-objective optimization. European Journal of Operational Research, 2006. 171(2): p. 463-495.
  122. Tan, K.C., E.F. Khor, and T.H. Lee, Multiobjective Evolutionary Algorithms And Applications: Algorithms and Applications. 2005: Springer.
  123. Tanev, I., Emergent generality of adapted locomotion gaits of simulated snake-like robot, in Genetic Programming, Proceedings. 2006. p. 85-96.
  124. Tanev, I., Learned mutation strategies in genetic programming for evolution and adaptation of simulated snakebot. Proceedings of the 2005 conference on Genetic and evolutionary computation, 2005: p. 687-694.
  125. Teo, J., Differential Evolution with Self-adaptive Populations. Lecture Notes in Computer Science, 2005. 3681: p. 1284.
  126. Teo, J., Evolutionary multi-objective optimization for automatic synthesis of artificial neural network robot controllers. Malaysian Journal of Computer Science, 2005. 18(2): p. 54-62.
  127. Teo, J., Exploring dynamic self-adaptive populations in differential evolution. Soft Computing-A Fusion of Foundations, Methodologies and Applications, 2006. 10(8): p. 673-686.
  128. Tsakonas, A., A comparison of classification accuracy of four genetic programming-evolved intelligent structures. Information Sciences, 2006. 176(6): p. 691-724.
  129. Upegui, A., C.A. Pena-Reyes, and E. Sanchez, An FPGA platform for on-line topology exploration of spiking neural networks. Microprocessors and Microsystems, 2005. 29(5): p. 211-223.
  130. Ursem, R.K., Differential Evolution Made Easy. 2005.
  131. Ventresca, M. and B. Ombuki, Search Space Analysis of Recurrent Spiking and Continuous-time Neural Networks. 2006, Brock Computer Science.
  132. Wang, G. and Y. Wang, A Game Model Based Co-evolutionary Algorithms for Multiobjective Optimization Problems.
  133. Wang, J.Y., Data Mining Analysis (breast-cancer data).
  134. Wang, Z. and B. Feng, Classification Rule Mining with an Improved Ant Colony Algorithm. Lecture Notes in Computer Science, 2004. 3339: p. 357–367.
  135. Weber, G.M., Data Representation and Algorithms For Biomedical Informatics Applications. 2005, Harvard University.
  136. Wiegand, S., C. Igel, and U. Handmann, Evolutionary multi-objective optimization of neural networks for face detection. International Journal of Computational Intelligence and Applications, 2004. 4(3): p. 237–253.
  137. Wong, M.L. and T. Mun, Evolving Recursive Programs by Using Adaptive Grammar Based Genetic Programming. Genetic Programming and Evolvable Machines, 2005. 6(4): p. 421-455.
  138. Xu, S. and M. Zhang, Data Mining—An Adaptive Neural Network Model for Financial Analysis. Information Technology and Applications, 2005. ICITA 2005. Third International Conference on, 2005. 1.
  139. Xue, F., multi-objective differential evolution: theory and applications. 2004, Rensselaer Polytechnic Institute.
  140. Xue, F., A.C. Sanderson, and R.J. Graves, Pareto-based multi-objective differential evolution. Evolutionary Computation, 2003. CEC'03. The 2003 Congress on, 2003. 2.
  141. Yan, W. and C.D. Clack, Behavioural GP diversity for dynamic environments: an application in hedge fund investment. Proceedings of the 8th annual conference on Genetic and evolutionary computation, 2006: p. 1817-1824.
  142. Yanai, K. and H. Iba, Probabilistic distribution models for EDA-based GP. Proceedings of the 2005 conference on Genetic and evolutionary computation, 2005: p. 1775-1776.
  143. Yao, X., Evolving Neural Network Ensembles by Minimization of Mutual Information. International Journal of Hybrid Intelligent Systems, 2004. 1(1): p. 12-21.
  144. Yao, X. and Y. Xu, Recent advances in evolutionary computation. Journal of Computer Science and Technology, 2006. 21(1): p. 1-18.
  145. Yeong, E.K., et al., Prediction of burn healing time using artificial neural networks and reflectance spectrometer. Burns, 2005. 31(4): p. 415-420.
  146. Yoo, I. and X. Hu, Biomedical Ontology MeSH Improves Document Clustering Qualify on MEDLINE Articles: A Comparison Study.
  147. Yoo, I. and X. Hu, A comprehensive comparison study of document clustering for a biomedical digital library MEDLINE. Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries, 2006: p. 220-229.
  148. Zaharie, D., Control of Population Diversity and Adaptation in Differential Evolution Algorithms. 9th International Conference on Soft Computing MENDEL, 2003.
  149. Zaharie, D., Critical Values for the Control Parameters of Differential Evolution Algorithms. Proc. of Mendel, 2002: p. 62-67.
  150. Zaharie, D., A Multipopulation Differential Evolution Algorithm for Multimodal Optimization. Proc. of Mendel. 4: p. 16-18.
  151. Zaharie, D., Parameter Adaptation in Differential Evolution by Controlling the Population Diversity. Proc. of 4th International Workshop on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, 2002: p. 385-397.
  152. Zaharie, D. and D. Petcu, Adaptive Pareto Differential Evolution and Its Parallelization. submitted to PPAM, 2003.
  153. Zhou, Z.H. and M.L. Zhang, Solving Multi-Instance Problems with Classifier Ensemble Based on Constructive Clustering.
  154. Zielinski, K., D. Peters, and R. Laur, Constrained Multi-Objective Optimization Using Differential Evolution.
  155. Zribi, N., et al., Optimization by Phases for the Flexible Job-shop Scheduling Problem. Control Conference, 2004. 5th Asian, 2004. 3.
 

Last update: Oct 10, 2006 02:01:05 PM ALAR: Artificial Life and Adaptive Robotics Lab