In the last decades there has been a steadily growing need and interest in computational methods for solving optimization problems with or without constraints. They play an important role in many fields (chemistry, mechanic, electrical, economic, etc.). Optimization techniques have been gaining greater acceptance in many industrial applications. This fact was motivated by the increased interest for improved economy in and better utilization of the existing material resources. Euler says: "Nothing happens in the universe that does not have a sense of either certain maximum or minimum". In this book we are primarily concerned with the use of learning automata as a tool for solving many optimization problems. Learning systems have made a significant impact on many areas of engineering problems including modelling, control, optimization, pattern recognition, signal processing and diagnosis. They are attractive and provide interesting methods for solving complex nonlinear problems characterized by a high level of uncertainty. Learning systems are expected to provide the capability to adjust the probability distribution on-line, based on the environment response. They are essentially feedback systems. The optimization problems are modeled as that of learning automaton or a hierarchical structure of learning automata operating in a random environment. W~e report new and efficient techniques to deal with different kinds (unconstrained, constrained) of stochastic optimization problems. The main advantage of learning automata over other optimization techniques is its general applicability, i.e., there are almost no condition concerning the function to be optimized (continuity, differentiability, convexity, unimodality, etc.).
In the last decade there has been a steadily growing need for and interest in computational methods for solving stochastic optimization problems with or wihout constraints. Optimization techniques have been gaining greater acceptance in many industrial applications, and learning systems have made a significant impact on engineering problems in many areas, including modelling, control, optimization, pattern recognition, signal processing and diagnosis. Learning automata have an advantage over other methods in being applicable across a wide range of functions. Featuring new and efficient learning techniques for stochastic optimization, and with examples illustrating the practical application of these techniques, this volume will be of benefit to practicing control engineers and to graduate students taking courses in optimization, control theory or statistics.
Deals with stochastic optimization problems and provides an introduction to learning automata. Covers unconstrained optimization problems, constrained optimization problems and optimization of nonstationary functions. Paper.