Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

List of Figures. List of Tables. Preface. Contributing Authors. Series Foreword. Part I: Foundations. 1. An Introduction to Evolutionary Algorithms J.A. Lozano. 2. An Introduction to Probabilistic Graphical Models P. Larranaga. 3. A Review on Estimation of Distribution Algorithms P. Larranaga. 4. Benefits of Data Clustering in Multimodal Function Optimization via EDAs J.M. Pena, et al. 5. Parallel Estimation of Distribution Algorithms J.A. Lozano, et al. 6. Mathematical Modeling of Discrete Estimation of Distribution Algorithms C. Gonzalez, et al. Part II: Optimization. 7. An Empiricial Comparison of Discrete Estimation of Distribution Algorithms R. Blanco., J.A. Lozano. 8. Results in Function Optimization with EDAs in Continuous Domain E. Bengoetxea, et al. 9. Solving the 0-1 Knapsack Problem with EDAs R. Sagarna, P. Larranaga. 10. Solving the Traveling Salesman Problem with EDAs V. Robles, et al. 11. EDAs Applied to the Job Shop Scheduling Problem J.A. Lozano, A. Mendiburu. 12. Solving Graph Matching with EDAs Using a Permutation-Based Representation E. Bengoetxea, et al. Part III: Machine Learning. 13. Feature Subset Selection by Estimation of Distribution Algorithms I. Inza, et al. 14. Feature Weighting for Nearest Neighbor by EDAs I. Inza, et al. 15. Rule Induction by Estimation of Distribution Algorithms B. Sierra, et al. 16. Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs L.M. de Campos, et al.17. Comparing K-Means, GAs and EDAs in Partitional Clustering J. Roure, et al. 18. Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms C. Cotta, et al. Index.

[1]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[2]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[3]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[4]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[5]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[6]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[7]  Thomas P. Caudell,et al.  Parametric Connectivity: Training of Constrained Networks using Genetic Algorithms , 1989, ICGA.

[8]  L. Darrell Whitley,et al.  Optimizing Neural Networks Using FasterMore Accurate Genetic Search , 1989, ICGA.

[9]  L. Darrell Whitley,et al.  Genetic algorithms and neural networks: optimizing connections and connectivity , 1990, Parallel Comput..

[10]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[11]  Luís B. Almeida,et al.  Speeding up Backpropagation , 1990 .

[12]  L. Darrell Whitley,et al.  Delta Coding: An Iterative Search Strategy for Genetic Algorithms , 1991, ICGA.

[13]  M. Kanehisa,et al.  A knowledge base for predicting protein localization sites in eukaryotic cells , 1992, Genomics.

[14]  Willfried Wienholt.  Minimizing the System Error in Feedforward Neural Networks with Evolution Strategy , 1993 .

[15]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Some Asymptotical Results from the (1,+ )-Theory , 1993, Evolutionary Computation.

[16]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: The (, )-Theory , 1994, Evolutionary Computation.

[17]  A. Dourado,et al.  Lime Kiln Fault Detection and Diagnosis by Neural Networks , 1995, ICANNGA.

[18]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.

[19]  Tim Jones.  Evolutionary Algorithms, Fitness Landscapes and Search , 1995 .

[20]  Markus Höhfeld,et al.  Improving the Generalization Performance of Multi-Layer-Perceptrons with Population-Based Incremental Learning , 1996, PPSN.

[21]  Thomas Bäck,et al.  Evolutionary Algorithms in Theory and Practice , 1996 .

[22]  H. Mühlenbein,et al.  From Recombination of Genes to the Estimation of Distributions I. Binary Parameters , 1996, PPSN.

[23]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[24]  L. D. Whitley.  A free lunch proof for Gray versus Binary encodings , 1999 .

[25]  Jorng-Tzong Horng,et al.  Incorporation family competition into Gaussian and Cauchy mutations to training neural networks using an evolutionary algorithm , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[26]  Sven Anderson,et al.  Training hidden Markov Models using population-based learning , 1999 .

[27]  Araceli Sanchis,et al.  Applying Evolution Strategies to Neural Networks Robot Controller , 1999, IWANN.

[28]  Pedro Ángel Castillo Valdivieso,et al.  G-Prop-II: global optimization of multilayer perceptrons using GAs , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[29]  José M. Molina,et al.  Neural networks robot controller trained with evolution strategies , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[30]  Pablo Moscato,et al.  Memetic algorithms: a short introduction , 1999 .

[31]  Byoung-Tak Zhang,et al.  Evolving neural trees for time series prediction using Bayesian evolutionary algorithms , 2000, 2000 IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks. Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks (Cat. No.00.

[32]  Pedro Larrañaga,et al.  A Review on Estimation of Distribution Algorithms , 2002, Estimation of Distribution Algorithms.