Theory Overview of Stochastic Optimization Algorithms -- General Remarks -- Exact Optimization Algorithms for Simple Problems -- Exact Optimization Algorithms for Complex Problems -- Monte Carlo -- Overview of Optimization Heuristics -- Implementation of Constraints -- Parallelization Strategies -- Construction Heuristics -- Markovian Improvement Heuristics -- Local Search -- Ruin & Recreate -- Simulated Annealing -- Threshold Accepting and Other Algorithms Related to Simulated Annealing -- Changing the Energy Landscape -- Estimation of Expectation Values -- Cooling Techniques -- Estimation of Calculation Time Needed -- Weakening the Pure Markovian Approach -- Neural Networks -- Genetic Algorithms and Evolution Strategies -- Optimization Algorithms Inspired by Social Animals -- Optimization Algorithms Based on Multiagent Systems -- Tabu Search -- Histogram Algorithms -- Searching for Backbones -- Applications -- General Remarks -- The Traveling Salesman Problem -- The Traveling Salesman Problem -- Extensions of Traveling Salesman Problem -- Application of Construction Heuristics to TSP -- Local Search Concepts Applied to TSP -- Next Larger Moves Applied to TSP -- Ruin & Recreate Applied to TSP -- Application of Simulated Annealing to TSP -- Dependencies of SA Results on Moves and Cooling Process -- Application to TSP of Algorithms Related to Simulated Annealing -- Application of Search Space Smoothing to TSP -- Further Techniques Changing the Energy Landscape of a TSP -- Application of Neural Networks to TSP -- Application of Genetic Algorithms to TSP -- Social Animal Algorithms Applied to TSP -- Simulated Trading Applied to TSP -- Tabu Search Applied to TSP -- Application of History Algorithms to TSP -- Application of Searching for Backbones to TSP -- Simulating Various Types of Government with Searching for Backbones -- The Constraint Satisfaction Problem -- The Constraint Satisfaction Problem -- Construction Heuristics for CSP -- Random Local Iterative Search Heuristics -- Belief Propagation and Survey Propagation -- Outlook -- Future Outlook of Optimization Business.

The search for optimal solutions pervades our daily lives. From the scientific point of view, optimization procedures play an eminent role whenever exact solutions to a given problem are not at hand or a compromise has to be sought, e.g. to obtain a sufficiently accurate solution within a given amount of time. This book addresses stochastic optimization procedures in a broad manner, giving an overview of the most relevant optimization philosophies in the first part. The second part deals with benchmark problems in depth, by applying in sequence a selection of optimization procedures to them. While having primarily scientists and students from the physical and engineering sciences in mind, this book addresses the larger community of all those wishing to learn about stochastic optimization techniques and how to use them.