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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ma, Xin-Xin Chen, Wei-Neng Yang, Qiang South China Univ Technol Sch Comp Sci & Engn Guangzhou Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728185262

Surrogated assisted evolutionary algorithms are commonly used to solve real-world expensive optimization problems. However, in some situations, no online data is available during the evolution process. In this situation, we have to build surrogate models based on offline historical data, which is known as offline data-driven optimization. Since no new data can be used to improve the surrogate models, offline data-driven optimization remains a challenging problem. In this paper, we propose a Gaussian process assisted offline estimation of multivariate Gaussian distribution algorithm to address the offline data-driven optimization problem. Instead of using surrogate models to predict the fitness values of individuals, we utilize a surrogate model to predict the rankings of individuals based on the frequently used lower confidence bound. In this way, the robustness of the proposed algorithm could be enhanced. Experiments are conducted on five commonly used benchmark problems. The experimental results demonstrate that the proposed offline surrogate model and the multivariate Gaussian estimation of distribution algorithm are able to achieve competitive performance.

关键词： offline data-driven optimization surrogate models Gaussian Regression Process multivariate Gaussian distribution estimation of distribution algorithms

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Process optimization of microbial fermentation with parameter uncertainties via distributionally robust discrete control

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JOURNAL OF PROCESS CONTROL 2023年 132卷

作者： Wang, Juan Chen, Chihua Zhao, Feiyan Wang, Jichao Li, An China Univ Petr Coll Sci Qingdao 266580 Shandong Peoples R China Xiamen Univ Sch Math Sci Xiamen 361005 Fujian Peoples R China

There are some uncertain kinetic parameters in microbial fermentation system because of the unclear intracel-lular metabolic mechanisms. Considering the affection of these uncertain parameters on system performance, dynamic process optimization of the fermentation system can be modeled as a distributionally robust discrete control problem under moment uncertainty, which aims to maximize the mean productivity by optimizing the discrete-valued dilution rate function. Based on duality theory, the established min-max discrete optimal control problem is first transformed into a single level minimization problem, which is then discretized into a large-scale parameter optimization problem with semi-infinite constraint via time transformation and control parameterization. A new two-step estimation of distribution algorithm is developed to solve the obtained large-scale optimization problem. Numerical results show the feasibility and effectiveness of the proposed solution approach together with the superiority of the obtained control strategy considering parameter uncertainties.

关键词： distributionally robust control Uncertainty estimation of distribution algorithms Duality theory Continuous fermentation

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DAE-GP: Denoising Autoencoder LSTM Networks as Probabilistic Models in estimation of distribution Genetic Programming

DAE-GP: Denoising Autoencoder LSTM Networks as Probabilistic...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Wittenberg, David Rothlauf, Franz Schweim, Dirk Johannes Gutenberg Univ Mainz Mainz Germany

ISBN: (纸本)9781450371285

estimation of distribution genetic programming (EDA-GP) algorithms are metaheuristics where sampling new solutions from a learned probabilistic model replaces the standard mutation and recombination operators of genetic programming (GP). This paper presents DAE-GP, a new EDA-GP which uses denoising autoencoder long short-term memory networks (DAE-LSTMs) as probabilistic model. DAE-LSTMs are artificial neural networks that first learn the properties of a parent population by mapping promising candidate solutions to a latent space and reconstructing the candidate solutions from the latent space. The trained model is then used to sample new offspring solutions. We show on a generalization of the royal tree problem that DAE-GP outperforms standard GP and that performance differences increase with higher problem complexity. Furthermore, DAE-GP is able to create offspring with higher fitness from a learned model in comparison to standard GP. We believe that the key reason for the high performance of DAE-GP is that we do not impose any assumptions about the relationships between learned variables which is different to previous EDA-GP models. Instead, DAE-GP flexibly identifies and models relevant dependencies of promising candidate solutions.

关键词： Genetic Programming estimation of distribution algorithms Denoising Autoencoders Long Short-Term Memory Networks

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Denoising autoencoder genetic programming: strategies to control exploration and exploitation in search

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2023年第2期24卷 17-17页

作者： Wittenberg, David Rothlauf, Franz Gagne, Christian Johannes Gutenberg Univ Mainz Dept Dermatol Mainz Rhineland Palat Germany Laval Univ Quebec City PQ Canada

Denoising autoencoder genetic programming (DAE-GP) is a novel neural network-based estimation of distribution genetic programming approach that uses denoising autoencoder long short-term memory networks as a probabilistic model to replace the standard mutation and recombination operators of genetic programming. At each generation, the idea is to capture promising properties of the parent population in a probabilistic model and to use corruption to transfer variations of these properties to the offspring. This work studies the influence of corruption and sampling steps on search. Corruption partially mutates candidate solutions that are used as input to the model, whereas the number of sampling steps defines how often we re-use the output during model sampling as input to the model. We study the generalization of the royal tree problem, the Airfoil problem, and the Pagie-1 problem, and find that both corruption strength and the number of sampling steps influence exploration and exploitation in search and affect performance: exploration increases with stronger corruption and lower number of sampling steps. The results indicate that both corruption and sampling steps are key to the success of the DAE-GP: it permits us to balance the exploration and exploitation behavior in search, resulting in an improved search quality. However, also selection is important for exploration and exploitation and should be chosen wisely.

关键词： Genetic programming estimation of distribution algorithms Probabilistic model-building Denoising autoencoders

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A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

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PROGRESS IN ARTIFICIAL INTELLIGENCE 2012年第1期1卷 103-117页

作者： Ceberio, Josu Irurozki, Ekhine Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Comp Sci & Artificial Intelligence Dept Intelligent Syst Grp Donostia San Sebastian Spain

estimation of distribution algorithms (EDAs) are a set of algorithms that belong to the field of Evolutionary Computation. Characterized by the use of probabilistic models to represent the solutions and the dependencies between the variables of the problem, these algorithms have been applied to a wide set of academic and real-world optimization problems, achieving competitive results in most scenarios. Nevertheless, there are some optimization problems, whose solutions can be naturally represented as permutations, for which EDAs have not been extensively developed. Although some work has been carried out in this direction, most of the approaches are adaptations of EDAs designed for problems based on integer or real domains, and only a few algorithms have been specifically designed to deal with permutation-based problems. In order to set the basis for a development of EDAs in permutation-based problems similar to that which occurred in other optimization fields (integer and real-value problems), in this paper we carry out a thorough review of state-of-the-art EDAs applied to permutation-based problems. Furthermore, we provide some ideas on probabilistic modeling over permutation spaces that could inspire the researchers of EDAs to design new approaches for these kinds of problems.

关键词： Evolutionary computation estimation of distribution algorithms Permutation-based optimization problems Probabilistic permutation modelling

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Copula estimation of distribution algorithms based on exchangeable Archimedean copula

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INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY 2012年第1期43卷 13-20页

作者： Wang, Lifang Guo, Xiaodong Zeng, Jianchao Hong, Yi Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan 030024 Peoples R China

The two key operators in estimation of distribution algorithms (EDAs) are estimating the distribution model according to the selected population and sampling new individuals from the estimated model. Copula EDA introduces the copula theory into EDA. The copula theory provides the theoretical basis and the way to separate the multivariate joint distribution probability function into a function called copula and the univariate margins. The estimation operator and the sampling operator in copula EDA are discussed in this paper, and three exchangeable Archimedean copulas are used in copula EDA. The experimental results show that the three copula EDAs perform equivalently to some classical EDAs.

关键词： evolutionary algorithms EAs estimation of distribution algorithms EDAs copula EDA copula theory exchangeable Archimedean copula

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An evolutionary correlation-aware feature selection method for classification problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年第0期75卷

作者： Namakin, Motahare Rouhani, Modjtaba Sabzekar, Mostafa Ferdowsi Univ Mashhad Dept Comp Engn Mashhad Iran Birjand Univ Technol Dept Comp Engn Birjand Iran

As global search techniques, population-based optimization algorithms have provided promising results in feature selection (FS) problems. However, their major challenge is high time complexity associated with the exploration of a large search space and consequently a large number of fitness function evaluations. Moreover, the interaction between features is another key issue in FS problems, directly affecting the classification per-formance through selecting correlated features. In this paper, an estimation of distribution algorithm (EDA)-based method is proposed with three important contributions. Firstly, as an extension of EDA, the proposed method in each iteration generates only two individuals competing based on a fitness function, evolving during the algorithm using our proposed update procedure. Secondly, we provide a guiding technique to determine the number of features to be selected for individuals in each iteration. As a result, the number of selected features in the final solution would be optimized during the evolution process. These two would lead to increasing the convergence speed of the algorithm. Thirdly, as the main contribution of the paper, in addition to considering the importance of each feature alone, the proposed method can consider the interaction between features, being able to deal with complementary features and consequently increase classification performance. To do this, we provide a conditional probability scheme that considers the joint probability distribution of selecting two fea-tures. The introduced probabilities successfully detect correlated features. Experimental results on a synthetic dataset with correlated features proved the performance of our proposed approach facing these types of features. Furthermore, the results on 13 real-world datasets obtained from the UCI repository showed the superiority of the proposed method in comparison with some state-of-the-art approaches. To evaluate the effectiveness of each feature subse

关键词： Feature selection Correlated features estimation of distribution algorithms Conditional probabilities

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A novel classification learning framework based on estimation of distribution algorithms

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2012年第4期3卷 353-366页

作者： Fan, Jiancong Xu, Qiang Liang, Yongquan Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao Shandong Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Comp Sci Qingdao Shandong Peoples R China

estimation of distribution algorithms (abbr. EDAs) is a relatively new branch of evolutionary algorithms. EDAs replace search operators with the estimation of the distribution of selected individuals + sampling from the population. In an EDAs, this explicit representation of the population is replaced with a probability distribution over the choices available at each position in the vector that represents a population member. In this paper, an estimation of distribution learning framework and the corresponding learning algorithm are proposed and the relevant properties of the framework are analysed on the basis of probability. The framework provides a basis and a principle criterion for designing and analysing evolutionary learning algorithms based on EDAs. The probability is the core tool of EDAs. EDA-based learning algorithms are required to estimate the population distribution by the sample distributions. The learning framework proposed can guide and regulate the design processes of learning algorithms and strategies based on EDAs. The framework involved in relevant learning problems is analysed from the perspectives of probability by properties analysis, proof and verification. The experiment results show that the framework proposed is feasible for realising learning from datasets and has better learning performances than some other relevant evolutionary learning methods.

关键词： evolutionary computation classification estimation of distribution algorithms EDAs machine learning

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Unbalanced Mallows Models for Optimizing Expensive Black-Box Permutation Problems

Unbalanced Mallows Models for Optimizing Expensive Black-Box...

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2nd Genetic and Evolutionary Computation Conference (GECCO)

作者： Irurozki, Ekhine Lopez-Ibanez, Manuel Telecom Paris Paris France Univ Malaga Malaga Spain

ISBN: (纸本)9781450383509

Expensive black-box combinatorial optimization problems arise in practice when the objective function is evaluated by means of a simulator or a real-world experiment. Since each fitness evaluation is expensive in terms of time or resources, the number of possible evaluations is typically several orders of magnitude smaller than in non-expensive problems. Classical optimization methods are not useful in this scenario. In this paper, we propose and analyze UMM, an estimation-of-distribution (EDA) algorithm based on a Mallows probabilistic model and unbalanced rank aggregation (uBorda). Experimental results on black-box versions of LOP and PFSP show that UMM outperforms the solutions obtained by CEGO, a Bayesian optimization algorithm for combinatorial optimization. Nevertheless, a slight modification to CEGO, based on the different interpretations for rankings and orderings, significantly improves its performance, thus producing solutions that are slightly better than those of UMM and dramatically better than the original version. Another benefit of UMM is that its computational complexity increases linearly with both the number of function evaluations and the permutation size, which results in computation times an order of magnitude shorter than CEGO, making it specially useful when both computation time and number of evaluations are limited.

关键词： Combinatorial optimization Bayesian optimization Expensive blackbox optimization estimation of distribution algorithms

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A copula-based estimation of distribution algorithm for calibration of microscopic traffic models

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2019年 98卷 449-470页

作者： Fard, Mehdi Rafati Mohaymany, Afshin Shariat Iran Univ Sci & Technol Sch Civil Engn Traff Res Lab Tehran Iran

The importance of calibration of microscopic traffic models as the main core of traffic simulation software results from the need for more realistic traffic behaviors. The latent essence of several parameters in such models as well as the uncertainties resulting from the noise in the data, make the process of calibration much more complex. Usually, the calibration process is formulated as an optimization problem. Selecting the appropriate solution algorithm due to nonlinear and non convex nature of the problem is crucial. The importance of the issue is more significant when the matter of calibrating the medium or large-scale simulation model is considered. This is mainly due to the expensive cost that running the simulation models impose. Therefore, applying the current algorithms in which finding the appropriate solutions requires a large number of simulation runs is not deemed suitable. In this paper, an estimation of distribution algorithm based on copula theory has been suggested. In contrast with traditional solution algorithms, in the proposed algorithm complex interaction between parameters of a model has been considered by constructing and sampling from a copula-based probabilistic model. Copulas are functions that describe the dependence structure of a set of random variables and connect multivariate distribution functions to one-dimensional marginal distribution functions. The results indicate that applying an explicit probabilistic model based on copula helps the estimation of distribution algorithm to explore the search space more effectively and efficiently as well as provides the possibility of extracting the knowledge with regard to the structure of the calibration problem through analyzing the probabilistic models that are constructed during the evolution process. Furthermore, this new algorithm has been compared with the genetic algorithm and kernel-based cross-entropy method on synthetic and real trajectory data. The results confirm that the propos

关键词： Calibration estimation of distribution algorithms Copula Dependency structure Static metrics

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