Results 11 to 20 of about 787,704 (278)
Multimodal estimation of distribution algorithms [PDF]
IEEE Transactions on Cybernetics, 2016 Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for crowding and speciation, two versions of this algorithm are developed ...
Chen, C. L. Philip +5 more
core +5 more sources
A review of estimation of distribution algorithms in bioinformatics [PDF]
BioData Mining, 2008 Evolutionary search algorithms have become an essential asset in the algorithmic toolbox for solving high-dimensional optimization problems in across a broad range of bioinformatics problems.
Armañanzas Rubén +10 more
doaj +7 more sources
Sub-structural Niching in Estimation of Distribution Algorithms [PDF]
Proceedings of the 7th annual conference on Genetic and evolutionary computation, 2005 We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level.
Abbass, H. A. +3 more
core +3 more sources
Fast Parabola Detection Using Estimation of Distribution Algorithms. [PDF]
Comput Math Methods Med, 2017 This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation.
Guerrero-Turrubiates JJ +8 more
europepmc +4 more sources
Theory of Estimation-of-Distribution Algorithms [PDF]
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2019 This is a preliminary version of a chapter in the upcoming book "Theory of Randomized Search Heuristics in Discrete Search Spaces", edited by Benjamin Doerr and Frank Neumann, to be published by ...
Krejca, Martin S., Witt, Carsten
+6 more sources
Significance-Based Estimation-of-Distribution Algorithms [PDF]
IEEE Transactions on Evolutionary Computation, 2018 Estimation-of-distribution algorithms (EDAs) are randomized search heuristics that create a probabilistic model of the solution space, which is updated iteratively, based on the quality of the solutions sampled according to the model. As previous works show, this iteration-based perspective can lead to erratic updates of the model, in particular, to ...
Doerr, Benjamin, Krejca, Martin
openaire +4 more sources
Bayesian Inference in Estimation of Distribution Algorithms [PDF]
, 2007 Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems.
Gallagher, Marcus +3 more
core +3 more sources
Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
Applied Sciences, 2020 Generating practical methods for simulation-based optimization has attracted a great deal of attention recently. In this paper, the estimation of distribution algorithms are used to solve nonlinear continuous optimization problems that contain noise. One
Abdel-Rahman Hedar +2 more
doaj +1 more source
Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms
2023 IEEE Congress on Evolutionary Computation (CEC), 2023 Variational quantum algorithms (VQAs) are hybrid approaches between classical and quantum computation, where a classical optimizer proposes parameter configurations for a quantum parametric circuit which is iteratively sampled. The overall performance of the algorithm depends on how the classical optimizer tunes the parameters of the quantum circuit ...
Soloviev, Vicente P. +2 more
openaire +2 more sources
Self-Adaptive Stable Mutation Based on Discrete Spectral Measure for Evolutionary Algorithms
Journal of Telecommunications and Information Technology, 2023 In this paper, the concept of a multidimensional discrete spectral measure is introduced in the context of its application to the real-valued evolutionary algorithms. The notion of a discrete spectral measure makes it possible to uniquely define a class
Andrzej Obuchowicz +1 more
doaj +1 more source