Stochastic simulation genetic algorithm

Other methods which are applied to conformational analysis and to generating multiple conformations and which can be regarded as random or stochastic techniques, since they explore the conformational space in a non-deterministic fashion, arc genetic algorithms (GA) [137, 1381 simulation methods, such as molecular dynamics (MD) and Monte Carlo (MC) simulations 1139], as well as simulated annealing [140], All of those approaches and their application to generate ensembles of conformations arc discussed in Chapter II, Section 7.2 in the Handbook. 

Two of the most popular stochastic methods are simulated annealing and genetic algorithms. 

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... 

II with a new chapter (for the second edition) on global optimization methods, such as tabu search, simulated annealing, and genetic algorithms. Only deterministic optimization problems are treated throughout the book because lack of space precludes discussing stochastic variables, constraints, and coefficients. 

The methods of simulated annealing (26), genetic algorithms (27), and taboo search (29) are three of the most popular stochastic optimization techniques, inspired by ideas from statistical mechanics, theory of evolutionary biology, and operations research, respectively. They are applicable to our current problem and have been used by researchers for computational library design. Because SA is employed in this chapter, a more-detailed description of the (generalized) SA is given below. 

Combined strategic and operational model 2 Genetic algorithm and simulation model to analyze stochastic effects... 

Laquerbe, C Laborde, J.C. Soares, S. Floquet, P. Pibouleau L. Domenech, S. Synthesis of RTD models via stochastic procedures simulated annealing and genetic algorithms. Comput. Chem. Eng. 2001, 25, 1169-1183. 

Many different methods have been developed for compound selection. They include selection based on clustering compounds, dissimilarity-based selection, selection based on partitioning a collection of compounds into some multidimensional space, experimental design methods and the use of stochastic methods such as simulated annealing and genetic algorithms. Filtering techniques are often employed prior to compound selection to remove undesirable compounds. 

To arrive at a true optimal subset of variables (wavelengths) for a given data set, consideration of all possible combinations should in principle be used but it is computationally prohibitive. Since each variable can either appear, or not, in the equation and since this is true with every variable, there are 2"-possible equations (subsets) altogether. For spectral data containing 500 variables, this means 2 possibilities. For this type of problems, i.e. for search of an optimal solution out of the millions possible, the stochastic search heuristics, such as Genetic Algorithms or Simulated Annealing, are the most powerful tools [14,15]. 

Hajela, P. (1997). Stochastic search in discrete structural optimization—Simulated annealing, genetic algorithms and neural networks. In W. Gutkowski (Ed.), Discrete Structural Optimization. CISM International Centre for Mechanical Sciences. Vol. 373. 

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