Optimization by Simulated Annealing

Kirkpatrick, Scott; Gelatt, C. D.; Vecchi, M.P.

doi:10.1016/b978-0-08-051581-6.50059-3

Cited by 6,686 publications

(8,638 citation statements)

References 15 publications

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“…The searches were not intelligent (that is not learning while progressing) leading to non-conclusive results. To search for a global optimum, we propose to use a non-convex optimisation algorithm, namely Simulated Annealing algorithm (Kirkpatrick et al, 1983), to do an intelligent search of the optimal code. The Simulated Annealing algorithm has had success in a wide variety application areas where the optimisation problem at hand is NP-hard, that is not solvable in polynomial time.…”

Section: Resultsmentioning

confidence: 99%

“…The Simulated Annealing algorithm has had success in a wide variety application areas where the optimisation problem at hand is NP-hard, that is not solvable in polynomial time. These application areas include the traveling salesman problem, graph partitioning, scheduling in operations research, VLSI circuit design in electronics, optimal source coder design in telecommunications, etc (Kirkpatrick et al, 1983;Kuruoglu and Ayanoglu, 1993). Simulated Annealing is motivated by experimental solid state physics where solids are first heated to a very high temperature and then cooled down slowly so that all electrons settle to their lowest energy states.…”

Section: Resultsmentioning

confidence: 99%

“…Simulated Annealing is motivated by experimental solid state physics where solids are first heated to a very high temperature and then cooled down slowly so that all electrons settle to their lowest energy states. The algorithm is motivated by the earlier ideas of Ulaby and Metropolis on chemical process modelling and is formulated by Kirkpatrick et al in Kirkpatrick et al (1983). Simulated Annealing proceeds with a series of random walks, namely Metropolis loops during which new configurations are proposed.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

The information capacity of the genetic code: Is the natural code optimal?

Kuruoğlu

Arndt

2017

Journal of Theoretical Biology

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A B S T R A C TWe envision the molecular evolution process as an information transfer process and provide a quantitative measure for information preservation in terms of the channel capacity according to the channel coding theorem of Shannon. We calculate Information capacities of DNA on the nucleotide (for non-coding DNA) and the amino acid (for coding DNA) level using various substitution models. We extend our results on coding DNA to a discussion about the optimality of the natural codon-amino acid code. We provide the results of an adaptive search algorithm in the code domain and demonstrate the existence of a large number of genetic codes with higher information capacity. Our results support the hypothesis of an ancient extension from a 2-nucleotide codon to the current 3-nucleotide codon code to encode the various amino acids.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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The information capacity of the genetic code: Is the natural code optimal?

Kuruoğlu

Arndt

2017

Journal of Theoretical Biology

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“…22 During iteration, a new random perturbation close to the current solution is evaluated and accepted with some probability depending on its fitness and a global parameter T called 'temperature' . This parameter controls the degree of randomness away from the current solution, and gradually decreases during the entire process.…”

Section: Elements Of Sa In Rffmentioning

confidence: 99%

Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies

Yang

2013

Eur J Hum Genet

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Genome-wide association studies (GWAS) has brought methodological challenges in handling massive high-dimensional data and also real opportunities for studying the joint effect of many risk factors acting in concert as an organic group. The random forest (RF) methodology is recognized by many for its potential in examining interaction effects in large data sets. However, RF is not designed to directly handle GWAS data, which typically have hundreds of thousands of single-nucleotide polymorphisms as predictor variables. We propose and evaluate a novel extension of RF, called random forest fishing (RFF), for GWAS analysis. RFF repeatedly updates a relatively small set of predictors obtained by RF tests to find globally important groups predictive of the disease phenotype, using a novel search algorithm based on genetic programming and simulated annealing. A key improvement of RFF results from the use of guidance incorporating empirical test results of genome-wide pairwise interactions. Evaluated using simulated and real GWAS data sets, RFF is shown to be effective in identifying important predictors, particularly when both marginal effects and interactions exist, and is applicable to very large GWAS data sets.

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“…However, when the inverse problem is non-convex, these approaches may fail to provide globally optimal solutions (Glowinski and Stocki, 1981;Linga et al, 2006). Global optimization methods such as simulated annealing (Eftaxias et al, 2002;Kirkpatrick et al, 1983), scatter search (RodriguezFernandez et al, 2006b), evolutionary computation (Fogel, 2000;Goldberg, 1989;Moles et al, 2003;Tsuchiya and Ross, 2001), and variants (Rodriguez-Fernandez et al, 2006a), are generally more reliable in these situations. The application of global methods has been reported in a few studies modeling metabolic networks (Himmelblau et al, 1967;Moles et al, 2003;Polisetty et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Diverse metabolic model parameters generate similar methionine cycle dynamics

Piazza

Xiao

Rabinowitz

et al. 2008

Journal of Theoretical Biology

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Parameter estimation constitutes a major challenge in dynamic modeling of metabolic networks. Here we examine, via computational simulations, the influence of system nonlinearity and the nature of available data on the distribution and predictive capability of identified model parameters. Simulated methionine-cycle metabolite concentration data (both with and without corresponding flux data) was inverted to identify model parameters consistent with it. Thousands of diverse parameter families were found to be consistent with the data to within moderate error, with most of the parameter values spanning over 1000-fold ranges irrespective of whether flux data was included. Due to strong correlations within the extracted parameter families, model predictions were generally reliable despite the broad ranges found for individual parameters. Inclusion of flux data, by strengthening these correlations, resulted in substantially more reliable flux predictions. These findings suggest that, despite the difficulty of extracting biochemically accurate model parameters from system level data, such data may nevertheless prove adequate for driving the development of predictive dynamic metabolic models.

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Optimization by Simulated Annealing

Cited by 6,686 publications

References 15 publications

The information capacity of the genetic code: Is the natural code optimal?

The information capacity of the genetic code: Is the natural code optimal?

Random forest fishing: a novel approach to identifying organic group of risk factors in genome-wide association studies

Diverse metabolic model parameters generate similar methionine cycle dynamics

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