Big Chemical Encyclopedia

Chemical substances, components, reactions, process design ...

Articles Figures Tables About

Algorithms, searching

Stochastic search methods. In all of the optimization methods discussed so far, the algorithm searches the objec-... [Pg.40]

The algorithm searches during a GA run for the fittest possible string. The fitness function defines a surface across which this search takes place (Figure 5.9) the surface is usually of considerable complexity because it is a function of all the variables that comprise the GA string. [Pg.122]

A. Jin, F. Leung, and D. F. Weaver, ]. Comput. Chem., 18, 1971 (1997). Development of a Genetic Algorithm Search Method (GAP1.0) for Exploring Peptide Conformational Space. [Pg.294]

The approach of Chaudhury el al,131 can in principle be combined with any of the search methods we have discussed in Section 2 (as long as the Hessian can be calculated relatively easy — this seems to be the critical point of the approach), but Chaudhury et a/.131 combined it with a genetic-algorithm search. Subsequently they demonstrated its validity for clusters of Lennard-Jones atoms. [Pg.311]

Mathematically, EFA provides initial estimates of the time profiles, often quite rough and of poor quality. More frequently, algorithms searching for the purest variables of D (e.g., SIMPLISMA [SIMPLe-to-use Interactive Selfmodeling Mixture Analysis]), applied either to the spectral or time domains, are used for finding the most characteristic profiles of the data set (47). [Pg.210]

Once an accurate wavefunction has been obtained in ab initio calculations, the forces on all the atoms in a cluster can be computed exactly and analytically using well-developed quantum-mechanical techniques. This ability enables us to carry out a full ab initio minimization of the cluster geometry and extract the optimal equilibrium geometry. In general, the optimization algorithm searches for a stationary point , that is, a molecular structure such that for all atomic coordinates the force is zero. Mathematically, this means that... [Pg.266]

The predominant approach to sequence motif discovery is the focused approach, which searches for novel motifs in a set of unaligned DNA or protein sequences suspected to contain a common motif. We discuss how the sequences can be selected in the next section. RE-based motif discovery algorithms for the focused approach search the space of all possible REs either exhaustively or heuristically (incompletely). Their objective is usually to identify the REs whose matches are most over-represented in the input sequences (relative to a background sequence model, randomly generated background sequences, or a set of negative control sequences). PWM-based motif discovery algorithms search the space of PWMs for motifs that maximize an objective function that is usually equal to (or related to) log likelihood ratio of the PWM ... [Pg.277]

In the classification setting, every example is assumed to be independent of every other example. However, there are cases where dependencies exist between examples and the label corresponding to such dependent examples forms a complex object. The structured-prediction problem attempts to predict a complex object such as a protein interaction network, phylogenetic tree, a binding site on a protein, etc. In short, a structured-prediction problem D is a classification problem where y e Y (the space of fhe label on an example) has a structure. For a finite set of data structures, the learning algorithm searches for the structured output that minimizes the expected cost E[c/,(j )] of example x [44] where cosf measures fhe dissimilarify between the example and a proposed structured output. [Pg.48]


See other pages where Algorithms, searching is mentioned: [Pg.298]    [Pg.367]    [Pg.779]    [Pg.675]    [Pg.36]    [Pg.33]    [Pg.156]    [Pg.186]    [Pg.129]    [Pg.78]    [Pg.127]    [Pg.79]    [Pg.67]    [Pg.163]    [Pg.220]    [Pg.274]    [Pg.6]    [Pg.12]    [Pg.779]    [Pg.587]    [Pg.399]    [Pg.253]    [Pg.657]    [Pg.334]    [Pg.340]    [Pg.123]    [Pg.104]    [Pg.196]    [Pg.197]    [Pg.110]    [Pg.743]    [Pg.1495]    [Pg.59]    [Pg.43]    [Pg.47]    [Pg.48]    [Pg.122]    [Pg.690]    [Pg.692]    [Pg.173]   


SEARCH



BLAST search algorithm

Bayesian search algorithms

Cambridge Structural Database search algorithms

Combinatorial search algorithms

Conformation search genetic algorithm

Conformational searching genetic algorithms

Database search algorithm

Database searching algorithms

FASTA search algorithm

Genetic Algorithms and Other Global Search Strategies

Genetic algorithm search method

Genetic algorithms conformational search problems

Global Search Algorithms

Grover search algorithm

INDEX search algorithm

Least-squares-based search algorithm

Mascot search algorithm

Match search algorithm

Maximal common substructure search algorithms

Minimal models search algorithm

Molecule, design search algorithm

Monte Carlo conformational search algorithms

Nelder-Mead search algorithm

Peak Search Algorithms

Peak-searching algorithms

Quantum search algorithm

Recommended Search Algorithms

Recursive Division the Split-search Algorithm

Saddle point search algorithms

Search algorithm

Search algorithm abstractions

Search algorithm evaluation

Search algorithm strategies

Search algorithms, limitations

Search evolutional algorithm

Search genetic algorithm

Searching Algorithm for

Searching algorithms, for response surfaces

Sequential Search Algorithm

Spectral search algorithms

Split-search algorithm

Steepest direction search algorithm

Substructures Searching Algorithm

Tabu search algorithms

The Search Algorithm

The quantum search algorithm

Tree-searching algorithm

© 2024 chempedia.info