SEARCH · PubMed Health

Results for “Algorithms”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,801 records · Page 100Linked to original sources

PubMed1999

Prospective randomized study of ablation and pacing versus medical therapy for paroxysmal atrial fibrillation: effects of pacing mode and mode-switch algorithm.

BACKGROUND: Atrioventricular (AV) node ablation and pacing has become accepted therapy for drug-refractory paroxysmal atrial fibrillation (PAF). However, few data demonstrate its superiority over continued medical therapy. The influence of pacing mode and mode-switch algorithm has not been investigated. METHODS AND RESULTS: Symptomatic patients who had tried >/=2 drugs for PAF were randomized to continue medical therapy (n=19) or AV junction ablation and implantation of dual-chamber mode-switching (DDDR/MS) pacemakers (slow algorithm [n=19] or fast algorithm [n=18]). Follow-up over 18 weeks was at 6-week intervals and used quality-of-life questionnaires (Psychological General Well Being [PGWB], McMaster Health Index [MHI], cardiac symptom score), exercise testing, echocardiography, and Holter monitoring. Paced patients were randomized to DDDR/MS or VVIR and subsequently crossed over. Ablation and DDDR/MS pacing produced better scores than drug therapy for overall symptoms (-41%, P<0.01), palpitations (-58%, P=0. 0001), and dyspnea (-37%, P<0.05). Changes in score from baseline were better with ablation and DDDR/MS pacing for overall symptoms (-48% versus -4%, P<0.005), palpitation (-62% versus -5%, P<0.001), dyspnea (-44% versus -3%, P<0.005), and PGWB (+12% versus +0.5%, P<0. 05). DDDR/MS was better than VVIR pacing for overall symptoms (-21%, P<0.05), dyspnea (-30%, P<0.005), and MHI (+5%, P<0.03). There were no differences between algorithms. More patients developed persistent AF with ablation and pacing than with drugs at 6 weeks (12 of 37 versus 0 of 19, P<0.01). CONCLUSIONS: Ablation and DDDR/MS pacing produces more symptomatic benefit than medical therapy or ablation and VVIR pacing but may result in early development of persistent AF.

Aged↗

PubMed2000

The early restart algorithm.

Consider an algorithm whose time to convergence is unknown (because of some random element in the algorithm, such as a random initial weight choice for neural network training). Consider the following strategy. Run the algorithm for a specific time T. If it has not converged by time T, cut the run short and rerun it from the start (repeat the same strategy for every run). This so-called restart mechanism has been proposed by Fahlman (1988) in the context of backpropagation training. It is advantageous in problems that are prone to local minima or when there is a large variability in convergence time from run to run, and may lead to a speed-up in such cases. In this article, we analyze theoretically the restart mechanism, and obtain conditions on the probability density of the convergence time for which restart will improve the expected convergence time. We also derive the optimal restart time. We apply the derived formulas to several cases, including steepest-descent algorithms.

Algorithms↗

PubMed2001

An algorithm for modifying neurotransmitter release probability based on pre- and postsynaptic spike timing.

The precise times of occurrence of individual pre- and postsynaptic action potentials are known to play a key role in the modification of synaptic efficacy. Based on stimulation protocols of two synaptically connected neurons, we infer an algorithm that reproduces the experimental data by modifying the probability of vesicle discharge as a function of the relative timing of spikes in the pre- and postsynaptic neurons. The primary feature of this algorithm is an asymmetry with respect to the direction of synaptic modification depending on whether the presynaptic spikes precede or follow the postsynaptic spike. Specifically, if the presynaptic spike occurs up to 50 ms before the postsynaptic spike, the probability of vesicle discharge is upregulated, while the probability of vesicle discharge is downregulated if the presynaptic spike occurs up to 50 ms after the postsynaptic spike. When neurons fire irregularly with Poisson spike trains at constant mean firing rates, the probability of vesicle discharge converges toward a characteristic value determined by the pre- and postsynaptic firing rates. On the other hand, if the mean rates of the Poisson spike trains slowly change with time, our algorithm predicts modifications in the probability of release that generalize Hebbian and Bienenstock-Cooper-Munro rules. We conclude that the proposed spike-based synaptic learning algorithm provides a general framework for regulating neurotransmitter release probability.

Action Potentials↗

PubMed2002

Factorial hidden Markov models and the generalized backfitting algorithm.

Previous researchers developed new learning architectures for sequential data by extending conventional hidden Markov models through the use of distributed state representations. Although exact inference and parameter estimation in these architectures is computationally intractable, Ghahramani and Jordan (1997) showed that approximate inference and parameter estimation in one such architecture, factorial hidden Markov models (FHMMs), is feasible in certain circumstances. However, the learning algorithm proposed by these investigators, based on variational techniques, is difficult to understand and implement and is limited to the study of real-valued data sets. This chapter proposes an alternative method for approximate inference and parameter estimation in FHMMs based on the perspective that FHMMs are a generalization of a well-known class of statistical models known as generalized additive models (GAMs; Hastie & Tibshirani, 1990). Using existing statistical techniques for GAMs as a guide, we have developed the generalized backfitting algorithm. This algorithm computes customized error signals for each hidden Markov chain of an FHMM and then trains each chain one at a time using conventional techniques from the hidden Markov models literature. Relative to previous perspectives on FHMMs, we believe that the viewpoint taken here has a number of advantages. First, it places FHMMs on firm statistical foundations by relating them to a class of models that are well studied in the statistics community, yet it generalizes this class of models in an interesting way. Second, it leads to an understanding of how FHMMs can be applied to many different types of time-series data, including Bernoulli and multinomial data, not just data that are real valued. Finally, it leads to an effective learning procedure for FHMMs that is easier to understand and easier to implement than existing learning procedures. Simulation results suggest that FHMMs trained with the generalized backfitting algorithm are a practical and powerful tool for analyzing sequential data.

Algorithms↗

PubMed2003

An efficient approximation algorithm for finding a maximum clique using Hopfield network learning.

In this article, we present a solution to the maximum clique problem using a gradient-ascent learning algorithm of the Hopfield neural network. This method provides a near-optimum parallel algorithm for finding a maximum clique. To do this, we use the Hopfield neural network to generate a near-maximum clique and then modify weights in a gradient-ascent direction to allow the network to escape from the state of near-maximum clique to maximum clique or better. The proposed parallel algorithm is tested on two types of random graphs and some benchmark graphs from the Center for Discrete Mathematics and Theoretical Computer Science (DIMACS). The simulation results show that the proposed learning algorithm can find good solutions in reasonable computation time.

Algorithms↗

PubMed2000

Building blocks, cohort genetic algorithms, and hyperplane-defined functions.

Building blocks are a ubiquitous feature at all levels of human understanding, froin perception through science and innovation. Genetic algorithms are designed to exploit this prevalence. A new, more robust class of genetic algorithms, cohort genetic algorithms (cGA's), provides substantial advantages in exploring search spaces for building blocks while exploiting building blocks already found. To test these capabilities, a new, general class of test functions, the hyperplane-defined functions (hdf's), has been designed. Hdf's offer the means of tracing the origin of each advance in performance; at the same time hdf's are resistant to reverse engineering, so that algorithms cannot be designed to take advantage of the characteristics of particular examples.

Algorithms↗

PubMed2002

Network random keys - a tree representation scheme for genetic and evolutionary algorithms.

When using genetic and evolutionary algorithms for network design, choosing a good representation scheme for the construction of the genotype is important for algorithm performance. One of the most common representation schemes for networks is the characteristic vector representation. However, with encoding trees, and using crossover and mutation, invalid individuals occur that are either under- or over-specified. When constructing the offspring or repairing the invalid individuals that do not represent a tree, it is impossible to distinguish between the importance of the links that should be used. These problems can be overcome by transferring the concept of random keys from scheduling and ordering problems to the encoding of trees. This paper investigates the performance of a simple genetic algorithm (SGA) using network random keys (NetKeys) for the one-max tree and a real-world problem. The comparison between the network random keys and the characteristic vector encoding shows that despite the effects of stealth mutation, which favors the characteristic vector representation, selectorecombinative SGAs with NetKeys have some advantages for small and easy optimization problems. With more complex problems, SGAs with network random keys significantly outperform SGAs using characteristic vectors. This paper shows that random keys can be used for the encoding of trees, and that genetic algorithms using network random keys are able to solve complex tree problems much faster than when using the characteristic vector. Users should therefore be encouraged to use network random keys for the representation of trees.

Algorithms↗

PubMed2003

Analysis of convergence of an evolutionary algorithm with self-adaptation using a stochastic Lyapunov function.

This paper analyses the convergence of evolutionary algorithms using a technique which is based on a stochastic Lyapunov function and developed within the martingale theory. This technique is used to investigate the convergence of a simple evolutionary algorithm with self-adaptation, which contains two types of parameters: fitness parameters, belonging to the domain of the objective function; and control parameters, responsible for the variation of fitness parameters. Although both parameters mutate randomly and independently, they converge to the "optimum" due to the direct (for fitness parameters) and indirect (for control parameters) selection. We show that the convergence velocity of the evolutionary algorithm with self-adaptation is asymptotically exponential, similar to the velocity of the optimal deterministic algorithm on the class of unimodal functions. Although some martingale inequalities have not be proved analytically, they have been numerically validated with 0.999 confidence using Monte-Carlo simulations.

Adaptation, Biological↗

PubMed2003

Genetic diversity as an objective in multi-objective evolutionary algorithms.

A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Diversity Evolutionary Algorithm (GDEA), strictly designed around GeDEM and then we compare it with other state-of-the-art algorithms on a well-established suite of test problems. Experimental results clearly indicate that the performance of GDEA is top-level.

Algorithms↗

PubMed2005

Drift and scaling in estimation of distribution algorithms.

This paper considers a phenomenon in Estimation of Distribution Algorithms (EDA) analogous to drift in population genetic dynamics. Finite population sampling in selection results in fluctuations which get reinforced when the probability model is updated. As a consequence, any probability model which can generate only a single set of values with probability 1 can be an attractive fixed point of the algorithm. To avoid this, parameters of the algorithm must scale with the system size in strongly problem-dependent ways, or the algorithm must be modified. This phenomenon is shown to hold for general EDAs as a consequence of the lack of ergodicity and irreducibility of the Markov chain on the state of probability models. It is illustrated in the case of UMDA, in which it is shown that the global optimum is only found if the population size is sufficiently large. For the needle-in-a haystack problem, the population size must scale as the square-root of the size of the search space. For the one-max problem, the population size must scale as the square-root of the problem size.

Algorithms↗

PubMed2005

RGFGA: an efficient representation and crossover for grouping genetic algorithms.

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.

Algorithms↗

PubMed2005

On the choice of the offspring population size in evolutionary algorithms.

Evolutionary algorithms (EAs) generally come with a large number of parameters that have to be set before the algorithm can be used. Finding appropriate settings is a difficult task. The influence of these parameters on the efficiency of the search performed by an evolutionary algorithm can be very high. But there is still a lack of theoretically justified guidelines to help the practitioner find good values for these parameters. One such parameter is the offspring population size. Using a simplified but still realistic evolutionary algorithm, a thorough analysis of the effects of the offspring population size is presented. The result is a much better understanding of the role of offspring population size in an EA and suggests a simple way to dynamically adapt this parameter when necessary.

Algorithms↗

PubMed1998

A rigorous complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs.

Evolutionary algorithms (EAs) are heuristic randomized algorithms which, by many impressive experiments, have been proven to behave quite well for optimization problems of various kinds. In this paper a rigorous theoretical complexity analysis of the (1 + 1) evolutionary algorithm for separable functions with Boolean inputs is given. Different mutation rates are compared, and the use of the crossover operator is investigated. The main contribution is not the result that the expected run time of the (1 + 1) evolutionary algorithm is theta (n ln n) for separable functions with n variables but the methods by which this result can be proven rigorously.

Algorithms↗

PubMed1999

FDA - a scalable evolutionary algorithm for the optimization of additively decomposed functions.

The Factorized Distribution Algorithm (FDA) is an evolutionary algorithm which combines mutation and recombination by using a distribution. The distribution is estimated from a set of selected points. In general, a discrete distribution defined for n binary variables has 2(n) parameters. Therefore it is too expensive to compute. For additively decomposed discrete functions (ADFs) there exist algorithms which factor the distribution into conditional and marginal distributions. This factorization is used by FDA. The scaling of FDA is investigated theoretically and numerically. The scaling depends on the ADF structure and the specific assignment of function values. Difficult functions on a chain or a tree structure are solved in about O(n radical n) operations. More standard genetic algorithms are not able to optimize these functions. FDA is not restricted to exact factorizations. It also works for approximate factorizations as is shown for a circle and a grid structure. By using results from Bayes networks, FDA is extended to LFDA. LFDA computes an approximate factorization using only the data, not the ADF structure. The scaling of LFDA is compared to the scaling of FDA.

Algorithms↗

PubMed2006

An extension of Geiringer's theorem for a wide class of evolutionary search algorithms.

The frequency with which various elements of the search space of a given evolutionary algorithm are sampled is affected by the family of recombination (reproduction) operators. The original Geiringer theorem tells us the limiting frequency of occurrence of a given individual under repeated application of crossover alone for the classical genetic algorithm. Recently, Geiringer's theorem has been generalized to include the case of linear GP with homologous crossover (which can also be thought of as a variable length GA). In the current paper we prove a general theorem which tells us that under rather mild conditions on a given evolutionary algorithm, call it A, the stationary distribution of a certain Markov chain of populations in the absence of selection is unique and uniform. This theorem not only implies the already existing versions of Geiringer's theorem, but also provides a recipe of how to obtain similar facts for a rather wide class of evolutionary algorithms. The techniques which are used to prove this theorem involve a classical fact about random walks on a group and may allow us to compute and/or estimate the eigenvalues of the corresponding Markov transition matrix which is directly related to the rate of convergence towards the unique limiting distribution.

Algorithms↗

PubMed2006

A fast learning algorithm for deep belief nets.

We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

Algorithms↗

PubMed2007

Fast generalized cross-validation algorithm for sparse model learning.

We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's fast relevance vector machine, Chen et al.'s orthogonal least squares, and Orr's regularized forward selection. The results demonstrate that the proposed algorithm is competitive.

Algorithms↗

PubMed2000

Application of an algorithm for the diagnosis of asthma in Chinese families: limitations and alternatives for the phenotypic assessment of asthma in family-based genetic studies.

Phenotype assessment is a crucial issue in gene mapping studies of asthma. Recently, Panhuysen and coworkers proposed an algorithm to define the asthma phenotype in gene mapping family-based studies. We classified members of 2,756 Chinese families ascertained on the basis of the presence of two or more siblings and no more than one parent with asthma using a slightly modified version of the aforementioned algorithm. Among 4,097 Chinese parents, 404 (9.9%) were classified as having "definite asthma," 284 (6.9%) as "probable asthma," 1,193 (29.1%) as "unclassifiable obstructive airway disease, " 626 (15.3%) as "COPD," and 1,590 (38.8%) as "unaffected" (no obstructive airway disease). Among 6,424 Chinese offspring, 1,065 (16.6%) were classified as having "definite asthma," 820 (12.8%) as "probable asthma," 1,996 (31.1%) as "unclassifiable obstructive airway disease," 228 (3.5%) as "COPD," and 2,315 (36%) as "unaffected." The use of the algorithm proposed by Panhuysen and coworkers in a Chinese population with a high prevalence of smoking would result in the exclusion of subjects with asthma who smoke or who have severe airflow obstruction from linkage analysis, as well as in an inability to explore any potential interactions between genetic factors and cigarette smoking in the pathogenesis of asthma. In the absence of a "gold standard," definitions of asthma that incorporate a combination of respiratory symptoms, increased airway responsiveness or bronchodilator response, and a physician's diagnosis of asthma are reasonable. The choice of a particular diagnostic algorithm for family-based genetic studies of asthma should be made according to factors such as the prevalence of smoking in the study population. Genetic studies of intermediate phenotypes related to asthma, which are objectively defined and may be influenced by a smaller number of genes, continue to be of great importance.

Adolescent↗

Refine your search to explore more results.