The Experts below are selected from a list of 97551 Experts worldwide ranked by ideXlab platform
Álvaro Fialho - One of the best experts on this subject based on the ideXlab platform.
-
Adaptive Strategy selection in differential evolution for numerical optimization an empirical study
Information Sciences, 2011Co-Authors: Wenyin Gong, Álvaro Fialho, Hui LiAbstract:Differential evolution (DE) is a versatile and efficient evolutionary algorithm for global numerical optimization, which has been widely used in different application fields. However, different strategies have been proposed for the generation of new solutions, and the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, we present two DE variants with Adaptive Strategy selection: two different techniques, namely Probability Matching and Adaptive Pursuit, are employed in DE to autonomously select the most suitable Strategy while solving the problem, according to their recent impact on the optimization process. For the measurement of this impact, four credit assignment methods are assessed, which update the known performance of each Strategy in different ways, based on the relative fitness improvement achieved by its recent applications. The performance of the analyzed approaches is evaluated on 22 benchmark functions. Experimental results confirm that they are able to Adaptively choose the most suitable Strategy for a specific problem in an efficient way. Compared with other state-of-the-art DE variants, better results are obtained on most of the functions in terms of quality of the final solutions and convergence speed.
-
Comparison-based Adaptive Strategy Selection with Bandits in Differential Evolution
2010Co-Authors: Álvaro Fialho, Raymond Ros, Marc Schoenauer, Michèle SebagAbstract:Differential Evolution is a popular powerful optimization algorithm for continuous problems. Part of its efficiency comes from the availability of several mutation strategies that can (and must) be chosen in a problem-dependent way. However, such flexibility also makes DE difficult to be automatically used in a new context. F-AUC-Bandit is a comparison-based Adaptive Operator Selection method that has been proposed in the GA framework. It is used here for the on-line control of DE mutation Strategy, thus preserving DE invariance w.r.t. monotonous transformations of the objective function. The approach is comparatively assessed on the BBOB test suite, demonstrating significant improvement on baseline and other Adaptive Strategy Selection approaches, while presenting a very low sensitivity to hyper-parameter setting.
-
GECCO (Companion) - Probability matching-based Adaptive Strategy selection vs. uniform Strategy selection within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10, 2010Co-Authors: Álvaro Fialho, Wenyin GongAbstract:Different strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PM-AdapSS-DE) [4], a method proposed to automatically select the mutation Strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform Strategy selection within the same sub-set of strategies.
-
Probability Matching-based Adaptive Strategy Selection vs. Uniform Strategy Selection within Differential Evolution: An Empirical Comparison on the BBOB-2010 Noiseless Testbed
2010Co-Authors: Álvaro Fialho, Wenyin Gong, Zhihua CaiAbstract:Different strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PMAdapSS-DE), a method proposed to automatically select the mutation Strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform Strategy selection within the same sub-set of strategies.
-
Fitness-AUC Bandit Adaptive Strategy Selection vs. the Probability Matching one within Differential Evolution: An Empirical Comparison on the BBOB-2010 Noiseless Testbed
2010Co-Authors: Álvaro Fialho, Marc Schoenauer, Michèle SebagAbstract:The choice of which of the available strategies should be used within the Differential Evolution algorithm for a given problem is not trivial, besides being problem-dependent and very sensitive with relation to the algorithm performance. This decision can be made in an autonomous way, by the use of the Adaptive Strategy Selection paradigm, that continuously selects which Strategy should be used for the next offspring generation, based on the performance achieved by each of the available ones on the current optimization process, i.e., while solving the problem. In this paper, we use the BBOB-2010 noiseless benchmarking suite to better empirically validate a comparison-based technique recently proposed to do so, the Fitness-based Area-Under-Curve Bandit, referred to as F-AUC-Bandit. It is compared with another recently proposed approach that uses Probability Matching technique based on the relative fitness improvements, referred to as PM-AdapSS-DE.
David L Darmofal - One of the best experts on this subject based on the ideXlab platform.
-
adjoint error estimation and grid adaptation for functional outputs application to quasi one dimensional flow
Journal of Computational Physics, 2000Co-Authors: David A Venditti, David L DarmofalAbstract:Abstract An error estimation and grid Adaptive Strategy is presented for estimating and reducing simulation errors in functional outputs of partial differential equations. The procedure is based on an adjoint formulation in which the estimated error in the functional can be directly related to the local residual errors of both the primal and adjoint solutions. This relationship allows local error contributions to be used as indicators in a grid-Adaptive Strategy designed to produce specially tuned grids for accurately estimating the chosen functional. In this paper, attention is limited to one-dimensional problems, although the procedure is readily extendable to multiple dimensions. The error estimation procedure is applied to a standard, second-order, finite volume discretization of the quasi-one-dimensional Euler equations. Both isentropic and shocked flows are considered. The chosen functional of interest is the integrated pressure along a variable-area duct. The error estimation procedure, applied on uniform grids, provides superconvergent values of the corrected functional. Results demonstrate that additional improvements in the accuracy of the functional can be achieved by applying the proposed Adaptive Strategy to an initially uniform grid. The proposed Adaptive Strategy is also compared with a standard Adaptive scheme based on the interpolation error in the computed pressure. The proposed scheme consistently yields more accurate functional predictions than does the standard scheme.
-
A multilevel error estimation and grid Adaptive Strategy for improving the accuracy of integral outputs
14th Computational Fluid Dynamics Conference, 1999Co-Authors: David A Venditti, David L DarmofalAbstract:An inexpensive error estimation and grid Adaptive Strategy is presented for estimating and reducing simulation errors in specific engineering outputs (functionals) such as lift or drag. These error estimates are directly related to the local residual errors of both the primal and adjoint solutions. This relationship allows the local error contributions to be used as indicators in a grid-Adaptive Strategy designed to produce specially tuned grids for accurately estimating the error in the chosen functional. Results are presented for the quasi-l-D Euler equations both isentropic and shocked flows. The remaining error in the functional, after correction, is superconvergent in all test cases. Additional improvements in the error estimates are obtained after applying the current Adaptive Strategy to an initially uniform grid.
Renaud Metereau - One of the best experts on this subject based on the ideXlab platform.
-
Nicaraguan peasant cooperativism in tension: Adaptive Strategy or counter-movement
Third World Quarterly, 2020Co-Authors: Renaud MetereauAbstract:Based on qualitative research conducted in three regions of Nicaragua, this paper examines the contribution of the communitarian approach to the new rurality in understanding the orientation and tensions within the peasant cooperative movement. The thematic analysis of 30 semi-structured interviews carried out with members of grassroots cooperatives reveals two main categories of motivation for engagement within the cooperative movement. A first set of motivations shows the will to transform the productive structures through small producer organisations to better adapt to the challenges imposed by global economic integration. A second set of motivations highlights broader socio-political objectives that seem to crystallise around the desire to build long-term alternatives to the exclusionary process of neoliberal globalisation. I explore these motivations in light of the distinction between reformist and communitarian approaches to the new rurality. I outline that the articulation of these two approaches, and more particularly the contribution of the communitarian approach, makes it possible to better understand the tensions within the cooperative movement in regard to socio-economic challenges. On this basis I call for a greater consideration of the communitarian dimensions of the new rurality to better define the role of the state, public policies and non-governmental organisations in supporting these phenomena." (source éditeur)
Wenyin Gong - One of the best experts on this subject based on the ideXlab platform.
-
Adaptive Strategy selection in differential evolution for numerical optimization an empirical study
Information Sciences, 2011Co-Authors: Wenyin Gong, Álvaro Fialho, Hui LiAbstract:Differential evolution (DE) is a versatile and efficient evolutionary algorithm for global numerical optimization, which has been widely used in different application fields. However, different strategies have been proposed for the generation of new solutions, and the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, we present two DE variants with Adaptive Strategy selection: two different techniques, namely Probability Matching and Adaptive Pursuit, are employed in DE to autonomously select the most suitable Strategy while solving the problem, according to their recent impact on the optimization process. For the measurement of this impact, four credit assignment methods are assessed, which update the known performance of each Strategy in different ways, based on the relative fitness improvement achieved by its recent applications. The performance of the analyzed approaches is evaluated on 22 benchmark functions. Experimental results confirm that they are able to Adaptively choose the most suitable Strategy for a specific problem in an efficient way. Compared with other state-of-the-art DE variants, better results are obtained on most of the functions in terms of quality of the final solutions and convergence speed.
-
GECCO (Companion) - Probability matching-based Adaptive Strategy selection vs. uniform Strategy selection within differential evolution: an empirical comparison on the bbob-2010 noiseless testbed
Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10, 2010Co-Authors: Álvaro Fialho, Wenyin GongAbstract:Different strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PM-AdapSS-DE) [4], a method proposed to automatically select the mutation Strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform Strategy selection within the same sub-set of strategies.
-
Probability Matching-based Adaptive Strategy Selection vs. Uniform Strategy Selection within Differential Evolution: An Empirical Comparison on the BBOB-2010 Noiseless Testbed
2010Co-Authors: Álvaro Fialho, Wenyin Gong, Zhihua CaiAbstract:Different strategies can be used for the generation of new candidate solutions on the Differential Evolution algorithm. However, the definition of which of them should be applied to the problem at hand is not trivial, besides being a sensitive choice with relation to the algorithm performance. In this paper, we use the BBOB-2010 noiseless benchmarking suite to further empirically validate the Probability Matching-based Adaptive Strategy Selection (PMAdapSS-DE), a method proposed to automatically select the mutation Strategy to be applied, based on the relative fitness improvements recently achieved by the application of each of the available strategies on the current optimization process. It is compared with what would be a timeless (naive) choice, the uniform Strategy selection within the same sub-set of strategies.
-
Adaptive Strategy selection in differential evolution
Genetic and Evolutionary Computation Conference, 2010Co-Authors: Wenyin Gong, Álvaro FialhoAbstract:Differential evolution (DE) is a simple yet powerful evolutionary algorithm for global numerical optimization. Different strategies have been proposed for the offspring generation; but the selection of which of them should be applied is critical for the DE performance, besides being problem-dependent. In this paper, the probability matching technique is employed in DE to autonomously select the most suitable Strategy while solving the problem. Four credit assignment methods, that update the known performance of each Strategy based on the relative fitness improvement achieved by its recent applications, are analyzed. To evaluate the performance of our approach, thirteen widely used benchmark functions are used. Experimental results confirm that our approach is able to Adaptively choose the suitable Strategy for different problems. Compared to classical DE algorithms and to a recently proposed Adaptive scheme (SaDE), it obtains better results in most of the functions, in terms of the quality of the final results and convergence speed.
Jens Markus Melenk - One of the best experts on this subject based on the ideXlab platform.
-
an Adaptive Strategy for hp fem based on testing for analyticity
Computational Mechanics, 2007Co-Authors: Tino Eibner, Jens Markus MelenkAbstract:We present an hp-Adaptive Strategy that is based on estimating the decay of the expansion coefficients when a function is expanded in L2-orthogonal polynomials on a triangle or a tetrahedron. We justify this approach by showing that the decay of the coefficients is exponential if and only if the function is analytic. Numerical examples illustrate the performance of this approach, and we compare it with two other hp-Adaptive strategies.
-
on residual based a posteriori error estimation in hp fem
Advances in Computational Mathematics, 2001Co-Authors: Jens Markus Melenk, Barbara WohlmuthAbstract:A family ηα, α∈[0,1], of residual-based error indicators for the hp-version of the finite element method is presented and analyzed. Upper and lower bounds for the error indicators ηα are established. To do so, the well-known Clement/Scott–Zhang interpolation operator is generalized to the hp-context and new polynomial inverse estimates are presented. An hp-Adaptive Strategy is proposed. Numerical examples illustrate the performance of the error indicators and the Adaptive Strategy.
