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This chapter describes Stochastic Algorithms.
The majority of the algorithms to be described in this book are comprised of probabilistic and stochastic processes. What differentiates the 'stochastic algorithms' in this chapter from the remaining algorithms is the specific lack of 1) an inspiring system, and 2) a metaphorical explanation. Both 'inspiration' and 'metaphor' refer to the descriptive elements in the standardized algorithm description.
These described algorithms are predominately global optimization algorithms and metaheuristics that manage the application of an embedded neighborhood exploring (local) search procedure. As such, with the exception of 'Stochastic Hill Climbing' and 'Random Search' the algorithms may be considered extensions of the multi-start search (also known as multi-restart search). This set of algorithms provide various different strategies by which 'better' and varied starting points can be generated and issued to a neighborhood searching technique for refinement, a process that is repeated with potentially improving or unexplored areas to search.
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