c_stochastic.tex

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% (c) Copyright 2010 Jason Brownlee. Some Rights Reserved.
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\chapter{Stochastic Algorithms}
\label{ch:stochastic}
\index{Stochastic Algorithms}
\index{Stochastic Global Optimization}

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% Overview
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\section{Overview}
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This chapter describes Stochastic Algorithms.

\subsection{Stochastic Optimization}
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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.

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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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%  Algorithms, one per section
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