Introduction to Stochastic Search and Optimization is an overview of the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The book may serve as either a reference book for researchers and practitioners or as a textbook, the latter use being supported by exercises at the end of every chapter and appendix. The text covers a broad range of the most widely used stochastic methods, including:

Random search· Recursive linear estimation· Stochastic approximation· Simulated annealing· Genetic and evolutionary algorithms· Machine (reinforcement) learning· Model selection· Simulation-based optimization· Markov chain Monte Carlo· Optimal experimental design
The MATLAB code here is in support of the book. Additional information on the book and MATLAB code is available at http://www.jhuapl.edu/ISSO/