Search algorithm strategies

There are several basic strategies for the improvement of the performance of substructure search algorithms ... 

EXMAT - A Linked Network of Expert Systems for Materials Analysis. Seven individual expert systems comprise EXMAT (1) problem definition and analytical strategy (2) instrumental configuration and conditions (3) data generation (4) chemometric/search algorithms (5) results (6) interpretation (7) analytical goals. Dynamic headspace (DHS)/GC and pyrolysis GC (PGC)/concentrators... 

L. Yamamoto, Evaluation of a catalytic search algorithm, in Proceeding of the 4th International Workshop on Nature Inspired Cooperative Strategies for Optimization, NICSO 2010, (Granada Spain, 2010), pp. 75-87... 

Mutation is another usually used operator of GA. Such an operation can be viewed as a transition from a current solution to its neighborhood solution in a local search algorithm. The main purpose is to ensure that premamre solutions resulting in only a local optimum will not occur. This study applied 3 mutation strategies (swap, flip, and slide mutation) which are shown in Fig. 6a, b, c, respectively. 

Constraints are typically applied as a penalty function that is added as an extra term in the scoring function, often as some simple function (e.g., harmonic) of the difference between the actual and target values. Other strategies are possible, however, and constraints have also been used systematically in the construction of model structures. This can be applied to distance constraints, where a buildup procedure is used to generate stmctures that satisfy all constraints [64]. Angle constraints can also be used to systematically search the conformational space, both using a branch-and-bound procedure [65] or in a tree-search algorithm in combination with distance constraints [66]. 

Note El mass spectral databases can optionally be included in the software package of mass spectrometers. Thus, the El spectra measured are directly searchable in the database by similarity search typically yielding a list of several hits. Alternatively, stand-alone solutions allow for sophisticated search algorithms and strategies (Table 5.2) [85-87]. 

Five main strategies have been used, often in combination, to improve the performance of substructure searching algorithms ... 

Feature-selection techniques can be generally considered as a combination of both a search algorithm and a criterion function. The solution to feature-selection problem is offered by the search algorithm, which generates a subset of features and compares them on the basis of the criterion function. From a computational viewpoint, an exhaustive search for the optimal solution becomes intractable even for moderate values of features (Siedlecki and Sklansky 1988). Despite these apparent difficulties, many feature-selection approaches have been developed (Serpico and Bruzzone 1994). The sequential forward selection (SFS) and the sequential backward selection (SBS) techniques are the simplest suboptimal search strategies they can identify the best feature subset achievable by adding (to an empty set in SFS) or removing (from the complete set from SBS) one feature at a time, until the desired number of features is achieved. 

The optimization strategy they propose can be understood as comprised of two components a feasibility test and an optimality search algorithm. 

Based on the database an adequate searching algorithm is used to find the most convenient operation line of control strategy for chosen criteria (e.g. highest system efficiency). 

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