Scalable Optimization via Probabilistic Modeling

• Corporate Author: SpringerLink (Online service)
• Other Authors: Pelikan, Martin (Editor), Sastry, Kumara (Editor), CantúPaz, Erick (Editor)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
• Series: Studies in Computational Intelligence, 33
• Subjects: Mathematics.; Artificial intelligence.; Probabilities.; Applied mathematics.; Engineering mathematics.; Probability Theory and Stochastic Processes.; Appl.Mathematics/Computational Methods of Engineering.; Artificial Intelligence (incl. Robotics).

This book focuses like a laser beam on one of the hottest topics in evolutionary computation over the last decade or so: estimation of distribution algorithms (EDAs). EDAs are an important current technique that is leading to breakthroughs in genetic and evolutionary computation and in optimization...

Table of Contents:

• The Factorized Distribution Algorithm and the Minimum Relative Entropy Principle
• Linkage Learning via Probabilistic Modeling in the Extended Compact Genetic Algorithm (ECGA)
• Hierarchical Bayesian Optimization Algorithm
• Numerical Optimization with Real-Valued Estimation-of-Distribution Algorithms
• A Survey of Probabilistic Model Building Genetic Programming
• Efficiency Enhancement of Estimation of Distribution Algorithms
• Design of Parallel Estimation of Distribution Algorithms
• Incorporating a priori Knowledge in Probabilistic-Model Based Optimization
• Multiobjective Estimation of Distribution Algorithms
• Effective and Reliable Online Classification Combining XCS with EDA Mechanisms
• Military Antenna Design Using a Simple Genetic Algorithm and hBOA
• Feature Subset Selection with Hybrids of Filters and Evolutionary Algorithms
• BOA for Nurse Scheduling
• Searching for Ground States of Ising Spin Glasses with Hierarchical BOA and Cluster Exact Approximation.