STOCHASTIC GLOBAL OPTIMIZATION

This book presents the main methodological and theoretical developments in stochastic global optimization. The extensive text is divided into four chapters; the topics include the basic principles and methods of global random search, statistical inference in random search, Markovian and population-based random search methods, methods based on statistical models of multimodal functions and principles of rational decisions theory.

Key features: Inspires readers to explore various stochastic methods of global optimization by clearly explaining the main methodological principles and features of the methods; Includes a comprehensive study of probabilistic and statistical models underlying the stochastic optimization algorithms; Expands upon more sophisticated techniques including random and semi-random coverings, stratified sampling schemes, Markovian algorithms and population based algorithms; Provides a thorough description of the methods based on statistical models of objective function; Discusses criteria for evaluating efficiency of optimization algorithms and difficulties occurring in applied global optimization.