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 more generally. I'm putting Scalable Optimization via Probabilistic Modeling in a prominent place in my library, and I urge you to do so as well. This volume summarizes the state of the art at the same time it points to where that art is going. Buy it, read it, and take its lessons to heart.

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.