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17th Genetic and Evolutionary Computation Conference (GECCO)

作者： Duc-Cuong Dang Lehre, Per Kristian Univ Nottingham Sch Comp Sci ASAP Res Grp Nottingham NG7 2RD England

ISBN: (纸本)9781450334723

estimation of distribution algorithms (EDA) are stochastic search methods that look for optimal solutions by learning and sampling from probabilistic models. Despite their popularity, there are only few rigorous theoretical analyses of their performance. Even for the simplest EDAs, such as the Univariate Marginal distribution algorithm (UMDA) which assumes independence between decision variables, there are only a handful of results about its runtime, and results for simple functions such as ONEMAX are still missing. In this paper, we show that the recently developed level-based theorem for non-elitist populations is directly applicable to runtime analysis of EDAs. To demonstrate this approach, we derive easily upper bounds on the expected runtime of the UMDA.

关键词： Runtime Analysis Level-based Analysis estimation of distribution algorithm

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The role of a priori information in the minimization of contact potentials by means of estimation of distribution algorithms

The role of a priori information in the minimization of cont...

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5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics (EvoBIO 2007)

作者： Santana, Roberto Larranaga, Pedro Lozano, Jose A. Univ Basque Country Dept Comp Sci & Artificial Intelligence Donostia San Sebastian Spain

ISBN: (纸本)9783540717829

Directed search methods and probabilistic approaches have been used as two alternative ways for computational protein design. This paper presents a hybrid methodology that combines features from both approaches. Three estimation of distribution algorithms are applied to the solution of a protein design problem by minimization of contact potentials. The combination of probabilistic models able to represent probabilistic dependencies with the use of information about residues interactions in the protein contact graph is shown to improve the efficiency of search for the problems evaluated.

关键词： estimation of distribution algorithm protein design energy minimization algorithms

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Caldwell, J. R. Watson, R. A. Univ Southampton Univ Rd Southampton SO17 1BJ Hants England

ISBN: (纸本)9781450349390

Model-building optimisation methods aim to learn the structure underlying a problem and exploit this to direct the exploration of solutions. This generally interleaves two processes: Generating samples (from the model), and updating the model (using selected samples). In most estimation of distribution algorithms (EDAs), e.g. BOA, selection is used only in the laSer, to determine which samples are retained for updating the model. In contrast, other evolution-inspired algorithms (such as rHN-G and MACRO) use selection differently - within the process that generates samples from the model. It has been hypothesised that this 'constructive selection' process can facilitate optimisation that other EDAs cannot but this has not been previously shown. Here we investigate these distinctions using constraint optimisation problems with a very simple modular structure. We find that a simple constructive selection method (rHN-g) can solve these problems in time polynomial in the problem size whereas other methods, such as BOA, require exponential time. We confirm that this result arises not from any difficulty in acquiring an accurate model but because of how samples are generated given the model. This suggests that by using constructive selection other EDAs could exploit the models they learn more efficiently to solve otherwise unsolvable problems.

关键词： Constructive selection Filter selection Model building optimisation estimation of distribution algorithm Multi-scale search Bayesian optimisation algorithm Restart Hopfield network with generative association Genetic algorithms Time-Complexity

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A Hybrid Multi-objective algorithm Using Genetic and estimation of distribution Based on Design of Experiments

A Hybrid Multi-objective Algorithm Using Genetic and Estimat...

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2009 IEEE International Conference on Intelligent Computing and Intelligent Systems(ICIS2009)

作者： Guangming Dai School of Computer China University of Geoscience Wuhan,China Jianwen Wang School of Computer China University of Geoscience Wuhan,China Jiankai Zhu School of Computer China University of Geoscience Wuhan,China

In this paper,we design a hybrid multi-objective algorithm using genetic and estimation of distribution based on design of *** first,we apply orthogonal design and uniform design to generate an initial population so that the population individual solutions scattered evenly in the feasible solutions ***,we proposed a new convergence criterion to check whether the distribution of population has the obvious *** the population is convergence,we use the model-based method to reproduce new individual solutions,otherwise genetic operator was employed to generate *** results of systematic experiments show that the hybrid algorithm this paper proposed capable of finding much better convergence near the Pareto-optimal solutions and better spread of solutions than RM-MEDA.

关键词： Multi-objective optimizing estimation of distribution algorithm Orthogonal design Uniform design Optimal Design of Constellation

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EDAspy: An extensible python package for estimation of distribution algorithms

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NEUROCOMPUTING 2024年 598卷

作者： Soloviev, Vicente P. Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Artificial Intelligence Dept Campus Montegancedo Madrid Spain

estimation of distribution algorithms (EDAs) are a type of evolutionary algorithms where a probabilistic model is learned and sampled in each iteration. EDAspy provides different state-of-the-art implementations of EDAs including the recent semiparametric EDA. The implementations are modularly built, allowing for easy extension and the selection of different alternatives, as well as interoperability with new components. EDAspy is totally free and open-source under the MIT license.

关键词： estimation of distribution algorithm Evolutionary algorithm Bayesian network Benchmarking

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Energy-efficient task scheduling with binary random faults in cloud computing environments

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Jin, Lei Yuan, Jie Zhou, Dequn Sang, Xiuzhi Chen, Shi Yu, Xianyu Lin, Guohui Nanjing Univ Aeronaut & Astronaut Coll Econ & Management Nanjing 211106 Peoples R China Nanjing Univ Aeronaut & Astronaut Res Ctr Soft Energy Sci Nanjing 211106 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

Fault management and energy consumption control have become focal topics in the rapid development cloud computing services. This paper addresses the task scheduling problem with binary random faults in networking and power supply of cloud computing environments and proposes a task scheduling model with multiobjectives of minimizing energy consumption and task completion time while maximizing task completion rate. An estimation of distribution algorithm (EDA) with crowding distance (C) and neighborhood search (N) (EDA-CN) is designed for the model, into which a multi-model probability matrix, regional dislocation backup mechanism, neighborhood search operator, and crowding distance operator are integrated. Numerical experiments examine the effectiveness of EDA-CN in comparison with EDA, EDA-C, and the classic non dominated sorting genetic algorithm III (NSGA3). The results show that EDA-CN consistently outperformed EDA and EDAC, and EDA-CN and NSGA3 performed comparably often yet EDA-CN still outperformed statistically significantly.

关键词： Cloud computing environment Green scheduling Fault management estimation of distribution algorithm

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Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search

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EPJ QUANTUM TECHNOLOGY 2024年第1期11卷 69页

作者： Soloviev, Vicente P. Dunjko, Vedran Bielza, Concha Larranaga, Pedro Wang, Hao Univ Politecn Madrid Computat Intelligence Grp CIG Madrid Spain Leiden Univ Appl Quantum Algorithms Leiden Leiden Netherlands Leiden Univ Leiden Inst Adv Comp Sci LIACS Leiden Netherlands

Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architecture is unknown until its parameters are tuned using classical routines. Moreover, the task becomes even more complicated since well-known trainability issues, e.g., barren plateaus (BPs), can occur. In this paper, we aim to achieve two improvements in QAS: (1) to reduce the number of measurements by an online surrogate model of the evaluation process that aggressively discards architectures of poor performance;(2) to avoid training the circuits when BPs are present. To detect the presence of the BPs, we employed a recently developed metric, information content, which only requires measuring the energy values of a small set of parameters to estimate the magnitude of cost function's gradient. The main idea of this proposal is to leverage a recently developed metric which can be used to detect the onset of vanishing gradients to ensure the overall search avoids such unfavorable regions. We experimentally validate our proposal for the variational quantum eigensolver and showcase that our algorithm is able to find solutions that have been previously proposed in the literature for the Hamiltonians;but also to outperform the state of the art when initializing the method from the set of architectures proposed in the literature. The results suggest that the proposed methodology could be used in environments where it is desired to improve the trainability of known architectures while maintaining good performance.

关键词： Quantum architecture search Evolutionary algorithm Information content Barren plateaus estimation of distribution algorithm Multi-level optimization Variational quantum eigensolver

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Harmless overfitting: using denoising autoencoders in estimation of distribution algorithms

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 2992-3022页

作者： Malte Probst Franz Rothlauf Honda Research Institute EU Offenbach Germany Information Systems and Business Administration Johannes Gutenberg-Universität Mainz Mainz Germany

estimation of distribution algorithms (EDAs) are metaheuristics where learning a model and sampling new solutions replaces the variation operators recombination and mutation used in standard Genetic algorithms. The choice of these models as well as the corresponding training processes are subject to the bias/variance tradeoff, also known as under- and overfitting: simple models cannot capture complex interactions between problem variables, whereas complex models are susceptible to modeling random noise. This paper suggests using Denoising Autoencoders (DAEs) as generative models within EDAs (DAE-EDA). The resulting DAE-EDA is able to model complex probability distributions. Furthermore, overfitting is less harmful, since DAEs overfit by learning the identity function. This overfitting behavior introduces unbiased random noise into the samples, which is no major problem for the EDA but just leads to higher population diversity. As a result, DAE-EDA runs for more generations before convergence and searches promising parts of the solution space more thoroughly. We study the performance of DAE-EDA on several combinatorial single-objective optimization problems. In comparison to the Bayesian Optimization algorithm, DAE-EDA requires a similar number of evaluations of the objective function but is much faster and can be parallelized efficiently, making it the preferred choice especially for large and difficult optimization problems.

关键词： denoising autoencoder estimation of distribution algorithm overfitting combinatorial optimization neural networks

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Pair-copula estimation of distribution algorithms

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2013年第2期4卷 186-196页

作者： Gao, Huimin Wang, Xiaoping Jiaxing Univ Mech & Elect Engn Coll Jiaxing 314001 Zhejing Peoples R China Jiaxing Univ Sch Business Jiaxing 314001 Zhejing Peoples R China

Copula theory provides a promising solution for the estimation of population probability in estimation distribution algorithms (EDAs), and more and more researchers pay attention to copula-EDAs. Most of the copula-EDAs researches are related to two variables case, in this paper, by taking advantage of the ability of pair-copula in high-dimensional correlation construction, a new algorithm is proposed, called pair-copula estimation distribution algorithms (pcEDAs). The architecture of pcEDAs is provided, and sampling method of the probability model is discussed, the simulation results based on two different vines, namely C-vine and D-vine, show that the proposed algorithm is not only feasible, but also perform very well.

关键词： pair-copula construction PCC vines estimation of distribution algorithm EDA

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Multiobjective meta-heuristic with iterative parameter distribution estimation for aeroelastic design of an aircraft wing

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ENGINEERING WITH COMPUTERS 2022年第1期38卷 695-713页

作者： Wansasueb, Kittinan Pholdee, Nantiwat Panagant, Natee Bureerat, Sujin Khon Kaen Univ Dept Mech Engn Sustainable Infrastructure Res & Dev Ctr Fac Engn Khon Kaen 40002 Thailand

This paper proposes a new self-adaptive meta-heuristic (MH) algorithm for multiobjective optimisation. The adaptation is accomplished by means of estimation of distribution. The differential evolution reproduction strategy is modified and used in this dominance-based multiobjective optimiser whereas population-based incremental learning is used to estimate the control parameters. The new method is employed to solve aeroelastic multiobjective optimisation of an aircraft wing which optimises structural weight and flutter speed. Design variables in the aeroelastic design problem include thicknesses of ribs, spars and composite layers. Also, the ply orientation of the upper and lower composite skins are assigned as the design variables. Additional benchmark test problems are also use to validate the search performance of the proposed algorithm. The performance validation reveals that the proposed optimiser is among the state-of-the-art multiobjective meta-heuristics. The concept of using estimation of distribution algorithm for tuning meta-heuristic control parameters is efficient and effective and becomes a new direction for improving MH performance.

关键词： Aeroelasticity Aircraft wing Surrogate models Multiobjective evolutionary algorithms estimation of distribution algorithm

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