Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
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发表时间：
2001-10
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通讯作者：
Pedro Larraanaga;Jose A. Lozano
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作者：
Pedro Larraanaga;Jose A. Lozano
图表列表。表格列表。序言。贡献作者。系列前言第一部分：基础。1.进化算法导论。洛萨诺2.概率图模型导论。3.分布估计算法综述. 4.数据聚类在多峰函数优化中的优势。佩纳等人5.分布的并行估计算法J. A. Lozano等人6.离散分布估计算法的数学建模。Gonzalez，et al. Part II：Optimization. 7.离散分布估计算法的经验比较R。布兰科，J.A.洛萨诺8.在连续域E中用EDA进行函数优化的结果。Bengoetxea等人9.用EDAs求解0-1背包问题。Sagarna，P. Larranaga. 10.用EDAs解决旅行商问题V. Robles，et al. 11. EDAs在车间作业调度问题中的应用。Lozano，A.门迪布鲁12.基于置换表示的EDA图匹配求解E。Bengoetxea等人，第三部分：机器学习。13.基于分布估计的特征子集选择算法I。Inza等人14.最近邻的特征加权EDAs I. Inza等人15.分布估计算法规则归纳B. Sierra等人16.贝叶斯网络中的部分溯因推理：遗传算法和进化算法的实证比较。de Campos，et al.17.在分区聚类中比较K-Means、GA和EDA J. Roure，et al. 18.用进化算法调整人工神经网络的权值C. Cotta，et al. Index.
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.