Locating global minima in optimization problems by a random-cost approach

Article Abstract:

A general purpose Monte Carlo procedure utilizing a random-cost approach may be used in an optimization algorithm that efficiently locates global minima. Straightforward search procedures are not effective for some functions where they are likely to be confused by local minima. The spin-glass problem and predicting the conformational ground state of proteins are two optimization problems for which the algorithm is useful. A one-dimensional random walk in the minimized function is enforced with local redirections of the search path.

Maxima and minima, Maxima and minima (Mathematics)

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New paths from dead ends

Article Abstract:

A new algorithm may allow protein models based on analysis of amino-acid sequence to be constructed more quickly by greatly reducing the number of possible combinations. The algorithm, devised by J. Desmet and colleagues, starts with a fixed backbone structure to which sets of side-chains are attached; a logical technique called dead-end elimination then keeps the number of alternative structures within manageable limits. The growing importance of protein modelling will probably make this algorithm very useful.

Proteins, Protein structure, Protein research, Amino acid sequence, Amino acid sequencing

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Inspiration for optimization from social insect behavior

Article Abstract:

Research is presented concerning the study of the behavior of an ant colony which has enabled the design and construction of computer models of optimization and distributed control algorithms. The applications of the models to science problems is discussed.

Behavior, Ants, Dynamic programming

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