Stochastic search method

Stochastic search methods. In all of the optimization methods discussed so far, the algorithm searches the objec-... 

Various search strategies can be used to locate the optimum. Indirect search strategies do not use information on gradients, whereas direct search strategies require this information. These methods always seek to improve the objective function in each step in a search. On the other hand, stochastic search methods, such as simulated annealing and genetic algorithms, allow some deterioration... 

The choice of prior distributions discussed in Section 2.2 has an impact on both the ease of implementation of the MCMC algorithm, and its speed of execution. Specific issues include the number of linear algebra operations and the rate at which stochastic search methods can explore the space of all subsets. 

Solution methods for optimization problems that involve only continuous variables can be divided into two broad classes derivative-free methods (e.g., pattern search and stochastic search methods) and derivative-based methods (e.g., barrier function techniques and sequential quadratic programming). Because the optimization problems of concern in RTO are typically of reasonably large scale, must be solved on-line in relatively small amounts of time and derivative-free methods, and generally have much higher computational requirements than derivative-based methods, the solvers contained in most RTO systems use derivative-based techniques. Note that in these solvers the first derivatives are evaluated analytically and the second derivatives are approximated by various updating techniques (e.g., BFGS update). 

Stochastic search methods offer a robust quality to optimisation processes. The most widely used stochastic search methods in the literature include genetic algorithms (GA), evolutionary strategies (ES), simulated annealing (SA) and tabu search (TS). The GA and ES are essentially the same (initially the former focused on discrete variables and the latter focused on continuous variables). They emulate nature s evolutionary behaviour, and the search evolves throughout... 

Chandrasekhar J, Saunders M, Jorgensen WL (2001) Effieitmt explmatimi of conformational space using the stochastic search method application to beta-peptide oligomers. J Comput Chem 22 1646-1654... 

The stochastic search method has been developed for the conformational analysis of organic molecules consisting of a few rings and some additional groups that generally have a limited number of low energy conformation. The approach... 

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