In this paper a computational approach of musical orchestration is presented. We consider orchestration as the search of relevant sound combinations within large instruments sample databases and propose two cooperatin...
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In this paper a computational approach of musical orchestration is presented. We consider orchestration as the search of relevant sound combinations within large instruments sample databases and propose two cooperating metaheuristics to solve this problem. Orchestration is seen here as a particular case of finding optimal constrained multisets on a large ensemble with respect to several objectives. We suggest a generic and easily extendible formalization of orchestration as a constrained multiobjective search towards a target timbre, in which several perceptual dimensions are jointly optimized. We introduce Orchid,e, a time-efficient evolutionary orchestration algorithm that allows the discovery of optimal solutions and favors the exploration of non-intuitive sound mixtures. We also define a formal framework for global constraints specification and introduce the innovative CDCSolver repair metaheuristic, thanks to which the search is led towards regions fulfilling a set of musical-related requirements. Evaluation of our approach on a wide set of real orchestration problems is also provided.
The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It consists in selecting a minimal set of test cases from a large test suite, ensuring that ...
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The Test Suite Minimization Problem (TSMP) is a NP-hard real-world problem that arises in the field of software engineering. It consists in selecting a minimal set of test cases from a large test suite, ensuring that the test cases selected cover a given set of requirements of a piece of software at the same time as it minimizes the amount of resources required for its execution. In this paper, we propose a Systolic Genetic Search (SGS) algorithm for solving the TSMP. SGS is a recently proposed optimization algorithm capable of taking advantage of the high degree of parallelism available in modern GPU architectures. The experimental evaluation conducted on a large number of test suites generated for seven real-world programs and seven large test suites generated for a case study from a real-world program shows that SGS is highly effective for the TSMP. SGS not only outperforms two competitive genetic algorithms, but also outperforms four heuristics specially conceived for this problem. The results also show that the GPU implementation of SGS has achieved a high performance, obtaining a large runtime reduction with respect to the CPU implementation for solutions with similar quality. The GPU implementation of SGS also shows an excellent scalability behavior when solving instances with a large number of test cases. As a consequence, the GPU-based SGS stands as a state of the art alternative for solving the TSMP in real-world software testing environments. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we analyze some technical issues concerning the use of Grid-enabled technologies based on the Globus Toolkit to solve multi-objective optimization problems. We develop two distributed algorithms: an enu...
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In this paper, we analyze some technical issues concerning the use of Grid-enabled technologies based on the Globus Toolkit to solve multi-objective optimization problems. We develop two distributed algorithms: an enumerative search and an evolutionary algorithm. The former is a simple technique, whose high time complexity is mastered down to acceptable execution times to some extent by the use of a Grid-enabled computing system such Globus. The second algorithm is an extension of PAES, a sequential evolutionary algorithm. The parallel results indicate that using Globus is a promising research line to solve multi-objective problems in Grid computing environments. (c) 2006 Elsevier B.V. All rights reserved.
Two fractional two-compartmental models are applied to the pharmacokinetics of articaine. Integer order derivatives are replaced by fractional derivatives, either of different, or of same orders. Models are formulated...
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Two fractional two-compartmental models are applied to the pharmacokinetics of articaine. Integer order derivatives are replaced by fractional derivatives, either of different, or of same orders. Models are formulated so that the mass balance is preserved. Explicit forms of the solutions are obtained in terms of the Mittag-Leffler functions. Pharmacokinetic parameters are determined by the use of the evolutionary algorithm and trust regions optimization to recover the experimental data.
Automated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning ...
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Automated machine learning (AutoML) and artificial neural networks (ANNs) have revolutionized the field of artificial intelligence by yielding incredibly high-performing models to solve a myriad of inductive learning tasks. In spite of their successes, little guidance exists on when to use one versus the other. Furthermore, relatively few tools exist that allow the integration of both AutoML and ANNs in the same analysis to yield results combining both of their strengths. Here, we present TPOT-NN-a new extension to the tree-based AutoML software TPOT-and use it to explore the behavior of automated machine learning augmented with neural network estimators (AutoML+NN), particularly when compared to non-NN AutoML in the context of simple binary classification on a number of public benchmark datasets. Our observations suggest that TPOT-NN is an effective tool that achieves greater classification accuracy than standard tree-based AutoML on some datasets, with no loss in accuracy on others. We also provide preliminary guidelines for performing AutoML+NN analyses, and recommend possible future directions for AutoML+NN methods research, especially in the context of TPOT.
The interaction between electrical and mechanical torques in the synchronous machines connected to bulk power transmission systems gives rise to electromechanical oscillations which, depending on the operating conditi...
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The interaction between electrical and mechanical torques in the synchronous machines connected to bulk power transmission systems gives rise to electromechanical oscillations which, depending on the operating conditions and type of disturbance, may be poorly damped or even unstable. Recently, a combination of power system stabilizers (PSSs) and power electronic devices known as FACTS (flexible alternating current transmission systems) has been recognized as one of the most effective alternatives to deal with the problem. Tuning such a combination of controllers, however, is a challenging task even for a very skilled engineer, due to the large number of parameters to be adjusted under several operating conditions. This paper proposes a hybrid method, based on a combination of evolutionary computation (performing a global search) and optimization techniques (performing a local search) that is capable of adequately tuning these controllers, in a fast and reliable manner, with minimum intervention from the human designer. The results show that the proposed approach provides fast, reliable and robust tuning of PSSs and FACTS devices for a problem in which both local and inter-area modes are targeted. (C) 2013 Elsevier Ltd. All rights reserved.
We formulate an integrated mathematical model for distribution of the injured on a three-type transportation network with destroyable independent links and with actual travel times considering two different groups of ...
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We formulate an integrated mathematical model for distribution of the injured on a three-type transportation network with destroyable independent links and with actual travel times considering two different groups of the injured. The aim is to find the temporary locations for aid stations and their capacities, the percentage of the injured with various severities allocated to each station and to different routes and the number of vehicles so that the total relief time is minimized. For this, we present a new approach, called circle-based approach, in which the effects of the disaster are considered as a number of concentric circular regions. The original problem is formulated as an integer nonlinear mathematical model. To solve large problems, two algorithms, an evolutionary algorithm based on simulated annealing and a discrete version of the imperialist competitive algorithm, are developed. An empirical study of the earthquakes in the city of Tehran, Iran, is conducted.
Path finding is used to solve the problem of finding a traversable path through an environment with obstacles. This problem can be seen in many different fields of study and these areas rely on fast and efficient path...
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Path finding is used to solve the problem of finding a traversable path through an environment with obstacles. This problem can be seen in many different fields of study and these areas rely on fast and efficient path finding algorithms. This paper aims to describe and review state of the art optimization techniques that are used on optimized path finding and compare their performances. Moreover, a special attention is paid on the proposed approaches to identify how they are tested on different test cases;whether the test cases are automatically generated or benchmark instances. The review opens avenues about the importance of automatic test case generation to test the different path finding algorithms.
Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of nu...
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Longitudinal dispersion coefficient (LDC) is known as the most remarkable environmental variables which plays a key role in evaluation of pollution profiles in water pipelines. Even though, there is a wide range of numerical models to estimate coefficient of longitudinal dispersion, these mathematical techniques may often come in quite few inaccuracies due to complex mechanism of convection-diffusion processes in pollutant transition in water pipelines. In this research work, to obtain more accurate prediction of LDC, general structure of group method of data handling (GMDH) is modified by means of extreme learning machine (ELM) conceptions. In fact, with getting inspiration from ELM, a novel GMDH method, called GMDH network based on using extreme learning machine (GMDH-ELM), is proposed in which weighting coefficients of quadratic polynomials applied in conventional GMDH are no longer required to be updated either using back propagation technique or other evolutionary algorithms through training stage. In fact, an intermediate parameter is employed to establish a relationship between the input and output in each neuron of the GMDH model. In this way, a well-known and reliable dataset (233 experimental data) related to LDC in water network pipelines, as output vector, is applied to conduct training and testing phases. Through datasets, the Re number, the average longitudinal flow velocity, the friction factor of pipeline and the diameter of pipe are considered as inputs of the proposed approach. The results of GMDH-ELM model indicate a highly satisfying level of precision in both training and testing phases. Furthermore, feed forward structure of GMDH model was improved by particle swarm optimization (PSO) and gravitational search algorithm (GSA) to predict LDC. Through a sound judgment, a comparison is drawn between the performance of GMDH-ELM and other developed GMDH models. Moreover, several empirical equations existing in literature have been applied for compari
evolutionary algorithms (EAs) are an important instrument for solving the multiobjective optimization problems (MOPs). It has been observed that the combined ant colony (MOEA/D-ACO) based on decomposition is very prom...
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evolutionary algorithms (EAs) are an important instrument for solving the multiobjective optimization problems (MOPs). It has been observed that the combined ant colony (MOEA/D-ACO) based on decomposition is very promising for MOPs. However, as the number of optimization objectives increases, the selection pressure will be released, leading to a significant reduction in the performance of the algorithm. It is a significant problem and challenge in the MOEA/D-ACO to maintain the balance between convergence and diversity in many-objective optimization problems (MaOPs). In the proposed algorithm, an MOEA/D-ACO with the penalty based boundary intersection distance (PBI) method (MOEA/D-ACO-PBI) is intended to solve the MaOPs. PBI decomposes the problems with many single-objective problems, a weighted vector adjustment method based on clustering, and uses different pheromone matrices to solve different single objectives proposed. Then the solutions are constructed and pheromone was updated. Experimental results on both CF1-CF4 and suits of C-DTLZ benchmarks problems demonstrate the superiority of the proposed algorithm in comparison with three state-of-the-art algorithms in terms of both convergence and diversity.
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