Probabilistic search

GAs are probabilistic search methods based on the mechanics of natural selection and genetics. The basic idea in using a GA as an optimization method is to represent a population of possible solutions in a chromosome-type encoding, called strings, and evaluate these encoded solutions through simulated reproduction, crossover, and mutation to reach an optimal or near-optimal solution. The GA starts with the creation of an initial population of... 

The Joint Entropy-based Diversity Analysis (JEDA) is a method to select representative subsets of compounds from combinatorial libraries by using a scoring function based on the Shannon s entropy and implemented in a probabilistic search algorithm [Landon and Schaus, 2006]. 

To select the optimal subset of compounds, that is, a set of compounds having the maximal chemical diversity, a probabilistic search algorithm is applied, which consists in selecting a subset of compounds based on a probability assigned to each compound. This algorithm optimizes the joint entropy (/H) of the subset of selected compounds. The task is performed iteratively, assigning each ith compound an initial uniform probability Pi = 1 fn, then calculating the score S that is added to the previous compound probability as... 

Define a CFG by hand and use a probabilistic search algorithm to find the parse or parses which is most likely given the input... 

The term evolutionary algorithm (EA) refers to a class of population based metaheuristic (probabilistic) optimization algorithms which imitate the Darwinian evolution ( survival ofthe fittest ). However, the biological terms are used as metaphors rather than in their exact meaning. The population of individuals denotes a set of solution candidates or points of the solution space. Each individual represents a point in the search space which is coded in the individual s representation (genome). The fitness of an individual is usually defined on the basis of the value of the objective function and determines its chances to stay in the population and to be used to generate new solution points. 

Circular substructures of various sorts have been widely used for applications such as structure and substructure searching, constitutional symmetry, structure elucidation and the probabilistic modeling of bioactivity inter alia The work reported here demonstrates that this type of fragment is also very well suited to virtual screening using multiple reference structures. 

The issue here is whether a warrantless search based on probabilistic rather than specific evidence violates the Fourth Amendment. 

Throughout the following section, different search methods and different parameters for a particular search method are compared. Comparing different search methods requires a performance measure, a probabilistic measure of satisfying well-defined criteria for successful search. A common performance measure is the enrichment function [20,21], Enrichment can be defined as the ratio of either concentrations or mole fractions before and after selection as a function of affinity or as a function of ligand rank in the library for example,... 

One more way to conduct a similarity-based virtual screening is to retrieve the structures containing a user-defined set of pharmacophoric features. In the Dynamic Mapping of Consensus positions (DMC) algorithm those features are selected by finding common positions in bit strings for all active compounds. The potency-scaled DMC algorithm (POT-DMC) " is a modification of DMC in which compounds activities are taken into account. The latter two methods may be considered as intermediate between conventional similarity search and probabilistic SAR approaches. 

Of course, the path followed during the optimization search is also strongly dependent on the frequency with which uphill moves are accepted according to the standard SA probabilistic criterion, written here as ... 

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