In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constra...
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In population-based optimization algorithms (POAs), given an optimization problem, the quality of the solutions depends heavily on the selection of algorithms, strategies and associated parameter combinations, constraint handling method, local search method, surrogate model, niching method, etc. In the literature, there exist several alternatives corresponding to each aspect of configuring a population-based algorithm such as one-point/two-points/uniform crossover operators, toumament/ranking/stochastic uniform sampling selection methods, Gaussian/Levy/Cauchy mutation operators, clearing/crowding/sharing based niching algorithms, adaptive penalty/epsilon/superiority of feasible constraint handling approaches, associated parameter values and so on. In POA literature, No Free Lunch (NFL) theorem has been well-documented and therefore, to effectively solve a given optimization problem, an appropriate configuration is necessary. But, the trial and error approach for the appropriate configuration may be impractical because at different stages of evolution, the most appropriate configurations could be different depending on the characteristics of the current search region for a given problem. Recently, the concept of incorporating ensemble strategies into POAs has become popular so that the process of configuring an optimization algorithm can benefit from both the availability of diverse approaches at different stages and alleviate the computationally intensive offline tuning. In addition, algorithmic components of different advantages could support one another during the optimization process, such that the ensemble of them could potentially result in a versatile POA. This paper provides a survey on the use of ensemble strategies in POAs. In addition, we also provide an overview of similar methods in the literature such as hyper-heuristics, island models, adaptive operator selection, etc. and compare them with the ensemble strategies in the context of POAs.
We consider the problem of data clustering using a heterogeneous ensemble with the use ofa co-association matrix. A probabilistic model is stated that takes into account the correlation ofevaluation functions with the...
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We consider the problem of data clustering using a heterogeneous ensemble with the use ofa co-association matrix. A probabilistic model is stated that takes into account the correlation ofevaluation functions with the help of which relationships are found between the characteristics ofthe ensemble and the quality indicators of the final solution. An expression for the optimalweights of basic algorithms for which the upper bound on the clustering error probability estimateis minimal is found. An experimental study of the proposed method has been carried out showingthe method to be advantageous over a number of similar methods.
This article suggests a technique for building an ensemble based on the DBSCAN algorithm. This technique uses the internal structure of a time series for adaptively selecting input parameters. When used in experiments...
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This article suggests a technique for building an ensemble based on the DBSCAN algorithm. This technique uses the internal structure of a time series for adaptively selecting input parameters. When used in experiments, it shows a narrower variance and higher levels of anomaly detection using real and synthetic data compared with a number of popular approaches.
Variant prioritization is a crucial step in the analysis of exome and genome sequencing. Multiple phenotype-driven tools have been developed to automate the variant prioritization process, but the efficacy of these to...
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Variant prioritization is a crucial step in the analysis of exome and genome sequencing. Multiple phenotype-driven tools have been developed to automate the variant prioritization process, but the efficacy of these tools in clinical setting with fuzzy phenotypic information and whether ensemble of these tools could outperform single algorithm remains to be assessed. A large rare disease cohort with heterogeneous phenotypic information, including a primary cohort of 1614 patients and a replication cohort of 1904 patients referred to exome sequencing, were recruited to assess the efficacy of variant prioritization and their ensemble. Three freely available tools-Exomiser, Xrare, and DeepPVP-and their ensemble were evaluated. The performance of all three tools was influenced by the attributes of phenotypic input. When combining these three tools by weighted-sum entropy method (EWE3), the ensemble outperformed any single algorithm, achieving a rate of 78% diagnostic variants in top 3 (13% improvement over current best performer, compared to Exomiser: 63%, Xrare: 65%, and DeepPVP: 51%), 88% in top 10 and 96% in top 30. The results were replicated in another independent cohort. Our study supports using entropy-weighted ensemble of multiple tools to improve variant prioritization and accelerate molecular diagnosis in exome/genome sequencing.
Taught systems such as artificial neural network (ANN) can be used for data interpretation of electric logging. Using only the ANN algorithm gives the result of coincidence between interpretable data and experimental ...
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ISBN:
(纸本)9781479941209
Taught systems such as artificial neural network (ANN) can be used for data interpretation of electric logging. Using only the ANN algorithm gives the result of coincidence between interpretable data and experimental results in certain samplings from 66% to 73%. But using additional algorithms of recognition and integrating the results of recognition could enhance quality of recognition by 1-3%. The problem of integration of classification algorithms results was formulated and realization as pseudocode was shown.
This paper investigates the effect to ensemble teaching learning-based optimization (TLBO) with other meta-heuristics methods like artificial bee colony (ABC), biogeography-based optimization (BBO), differential evolu...
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