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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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A variable neighbourhood search enhanced estimation of distribution algorithm for quadratic assignment problems

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OPSEARCH 2021年第1期58卷 203-233页

作者： Pradeepmon, T. G. Panicker, Vinay V. Sridharan, R. Natl Inst Technol Calicut Dept Mech Engn Kozhikode Kerala India Muthoot Inst Technol & Sci Dept Mech Engn Ernakulam Kerala India

Quadratic Assignment Problem (QAP) is one of the most complex combinatorial optimization problems. Many real-world problems such as printed circuit board design, facility location problems, assigning gates to airplanes can be modelled as QAP. Problems of size greater than 35 is not able to solve optimally using conventional optimization methods. This warrants the use of evolutionary optimization methods for obtaining optimal or near optimal solutions for QAPs. This work proposes a hybridization on a univariate estimation of distribution Algorithm, namely the Population Based Incremental Learning Algorithm (PBILA), with Variable Neighbourhood Search (VNS) for solving QAPs. The proposed algorithm is employed to solve benchmark instances of QAP and the results are reported. The results of this work reveals that PBILA on its own is not efficient for solving the QAPs. However, when hybridised with VNS, the algorithm performs well providing best known solutions for 95 test instances out of the 101 instances considered. For most of the test instances, the percentage deviation is less than one percentage. The overall average percentage deviation of the obtained solutions from the best-known solutions is 0.037%, which is a significant improvement when compared with state-of-the-art algorithms.

关键词： Quadratic assignment problem estimation of distribution algorithms Variable neighbourhood search

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Structural Difficulty in estimation of distribution Genetic Programming 11

Structural Difficulty in Estimation of Distribution Genetic ...

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13th Annual Genetic and Evolutionary Computation Conference (GECCO)

作者： Kim, Kangil Kim, Min Hyeok McKay, Bob Seoul Natl Univ Struct Complex Lab Seoul 151744 South Korea

ISBN: (纸本)9781450305570

estimation of distribution algorithms were introduced into Genetic Programming over 15 years ago, and have demonstrated good performance on a range of problems, but there has been little research into their limitations. We apply two such algorithms - scalar and vectorial Stochastic Grammar GP - to Daida's well-known Lid problem, to better understand their ability to learn specific structures. The scalar algorithm performs poorly, but the vectorial version shows good overall performance. We then extended Daida's problem to explore the vectorial algorithm's ability to find even more specific structures, finding that the performance fell off rapidly as the specificity of the required structure increased. Thus although this particular system has less severe structural difficulty issues than standard GP, it is by no means free of them. Track: Genetic Programming

关键词： estimation of distribution algorithms Genetic Programming Structural Difficulty

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Introducing Generative adversarial networks on estimation of distribution algorithm to solve permutation-based problems

Introducing Generative adversarial networks on Estimation of...

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International Symposium on Innovative Informatics of Biskra (ISNIB)

作者： Lemtenneche, Sami Cheriet, Abdelhakim Bensayah, Abdallah Kasdi Merbah Univ Ouargla Algeria

ISBN: (纸本)9798350320657

As a subclass of evolutionary algorithms, estimation of distribution algorithms (EDAs) have found widespread use in a variety of optimization problems with impressive performance. In each generation, they build a probabilistic model representing the promising individuals, and the next generation constructs by sampling this model. Constructing and sampling the probabilistic model is a real challenge in developing such algorithms. Generative adversarial networks (GANs) are a popular kind of generative model that has been widely adopted due to their ability to generate new samples that closely match the distribution of training data. However, research on how GANs handle permutation spaces is lacking. We suggest a novel estimation of distribution Algorithm (EDA) that uses GANs as a probabilistic model estimator to advance this subject. in order to preserve the information captured from the selected individuals, those promising individuals were represented by a one-hot matrix, then used to train GANs. The proposed algorithm is tested to solve two permutation problems: The Travelling Salesman Problem (TSP) and Permutation Flowshop Scheduling Problem (PFSP). The Experimental results show that the proposed algorithm can obtain the optimal solution in some instances and a near-optimal in others.

关键词： Deep Generative Models Permutation Travelling Salesman Problem estimation of distribution algorithms

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An intelligent scatter with estimation of distribution for tabu search

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11th International Conference on Computer Aided Systems Theory

作者： Munetomo, Masaharu Satake, Yuta Akama, Kiyoshi Hokkaido Univ Informat Initiat Ctr Sapporo Hokkaido 0600811 Japan Hokkaido Univ Grad Sch Informat Sci & Technol Sapporo Hokkaido 0600811 Japan

ISBN: (纸本)9783540758662

This paper proposes an efficient optimization algorithm based on tabu search and estimation of distribution algorithms. The proposed algorithm estimates characteristics of distribution of solutions after performing tabu searches based on a marginal product model to acquire linkage information. Once correct linkage information is obtained, we can perform crossovers effectively without disrupting building blocks using the information. The proposed algorithm is expected to adapt to both global and local characteristics of solution space. Through empirical studies, we show the effectiveness of our approach.

关键词： tabu search extended compact genetic algorithm estimation of distribution algorithms

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A Novel Fuzzy Histogram based estimation of distribution Algorithm for Global Numerical Optimization

A Novel Fuzzy Histogram based Estimation of Distribution Alg...

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International Conference of Soft Computing and Pattern Recognition

作者： Liu, Weili Zhong, Jing-hui Wu, Wei-gang Xiao, Jing Zhang, Jun Sun Yat Sen Univ Dept Comp Sci Guangzhou 510275 Guangdong Peoples R China

ISBN: (纸本)9781424453306

Applying estimation of distribution algorithms (EDAs) to solve continuous problems is a significant and challenging task in the field of evolutionary computation. So far, various continuous EDAs have been developed based on different probability models. Initially, the EDAs based on a single Gaussian probability model are widely used but they have trouble in solving multimodal problems. Later EDAs based on a mixture model and on a clustering technique are then introduced to conquer such drawback. However, they are either time consuming or need prior knowledge of the problems. Recently, the histogram has begun to be used in continuous EDAs, but the histogram based EDAs (HEDAs) usually need too much time and space to gain a highly accurate solution. On the basis of pioneering contributions, this paper proposes a fuzzy histogram based EDA (FHEDA) for continuous optimization. In the FHEDA, the estimated range of the fuzzy histogram is adjusted adaptively by the current promising solutions, which leads the algorithm to search good solutions efficiently. A mutation mechanism is also introduced in the sampling operation to avoid being trapped in local optima. The performance of the proposed FHEDA is evaluated by testing seven benchmark functions with different characteristics. Two Gaussian based EDAs and the sur-shr-HEDA are studied for comparison. The results show that among all experimental algorithms, the FHEDA can give comparatively satisfying performance on unimodal and multimodal functions.

关键词： estimation of distribution algorithms Fuzzy Histogram Numerical Optimization Evolutionary algorithms

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estimation of distribution using Population Queue based Variational Autoencoders

Estimation of Distribution using Population Queue based Vari...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Bhattacharjee, Sourodeep Gras, Robin Univ Windsor Sch Comp Sci Windsor ON Canada Univ Windsor Sch Comp Sci Dept Biol Sci Great Lakes Inst Environm Res Windsor ON Canada

ISBN: (纸本)9781728121536

We present a new estimation of distribution algorithms (EDA) based on two novel Variational Autoencoders generative model building algorithms. The first method, Variational Autoencoder with Population Queue (VAE-EDA-Q), employs a queue of historical populations, which is updated at each iteration of EDA in order to smooth the data generation process. The second method uses Adaptive Variance Scaling (AVS) with VAE-EDA-Q to dynamically update the variance at which the probabilistic model is sampled. The results obtained prove our methods to be significantly more computationally efficient than state-of-the-art algorithms and perform significantly less number of fitness evaluations when tested on benchmark problems such as Trap-k and NK Landscapes. Moreover, we report results of applying our approach successfully to highly complex problems such as Trap 11, Trap 13, and NK Landscapes with neighborhood size K = 8 and K = 10.

关键词： estimation of distribution algorithms Variational Autoencoders Machine Learning Combinatorial Optimization

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A Layered Learning estimation of distribution Algorithm 22

A Layered Learning Estimation of Distribution Algorithm

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Li, Yong Yang, Qiang Gao, Xu-Dong Lu, Zhen-Yu Zhang, Jun Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing Peoples R China Hanyang Univ Dept Elect & Elect Engn Ansan South Korea Chaoyang Univ Technol Dept Comp Sci & Informat Engn Taichung Taiwan

ISBN: (纸本)9781450392686

Though estimation of distribution algorithms (EDAs) have witnessed success in problem optimization, most of them suffer from sharp shrink of covariance, which may lead to premature convergence. To alleviate this issue, this paper proposes a layered learning estimation of distribution algorithm (LLEDA) by maintaining multiple probability distribution models. Specifically, LLEDA first separates the population into several layers based on fitness. Then, the mean position of each layer is computed. Subsequently, we let the estimated mean position in each lower layer randomly learn from the one of a randomly selected higher layer, so that the mean positions of lower layers could be promoted to be close to promising areas found in the current population. At last, the covariance of each layer is estimated based on the generated new mean position and the individuals in this layer. By this means, multiple probability models with high quality are maintained and then are used to sample promising and diversified offspring separately. Comparative experiments conducted on a widely used benchmark problem set demonstrate that the proposed LLEDA achieves competitive or even much better performance than several state-of-the-art and representative EDAs.

关键词： estimation of distribution algorithms Multivariate Gaussian distribution Layered Learning Multiple distribution Models Global Numerical Optimization

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An New estimation of distribution Algorithm Based Edge Histogram Model for Flexible Job-Shop Problem

An New Estimation of Distribution Algorithm Based Edge Histo...

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International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE)

作者： He, Xiaojuan Zeng, Jianchao Xue, Songdong Wang, Lifang Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou Peoples R China Taiyuan Univ Sci & Technol Complex Syst & Computat Intelligence Lab Taiyuan Peoples R China

ISBN: (纸本)9783642226939

An estimation of distribution algorithm for flexible job shop scheduling problem was proposed. The probability model was given using frequency information of pair-wise operations neighboring. Then the structure of optimal individual was marked and the operations of optimal individual were partitioned to some independent sub-blocks. Each sub-block was taken as a whole to be adjusted to avoid repeating search in same area and improve search speed. The experimental results show that the proposed algorithm is efficient for Flexible Job-Shop Problems.

关键词： FJSP estimation of distribution algorithms Neighboring Operations Probability Model

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estimation of distribution Algorithm using Factor Graph and Markov Blanket Canonical Factorization 14

Estimation of Distribution Algorithm using Factor Graph and ...

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16th Genetic and Evolutionary Computation Conference (GECCO)

作者： Helmi, B. Hoda Rahmani, Adel T. Iran Univ Sci & Technol Tehran *** Iran

ISBN: (纸本)9781450326629

Finding a good model and efficiently estimating the distribution is still an open challenge in estimation of distribution algorithms (EDAs). Factorization encoded by models in most of the EDAs are constrained. However for optimization of many real-world problems, finding the model capable of representing complex interactions without much computational complexity overhead is the key challenge. On the other hand factor graph which is the most natural graphical model for representing additively decomposable functions is rarely employed in EDAs. In this paper we introduce Factor Graph based EDA (FGEDA) which learns factor graph as the model and estimate the probability distribution represented by the learned factor graph using Markov blanket canonical factorization. The class of factorization that is employed for approximation of distribution in FGEDA is expanded relative to famous EDAs. We have used matrix factorization for learning the factor graph of the problem based on the pairwise mutual information between pair of variables. Gibbs sampling and BB- wise crossover are used to generate new samples. Empirical evaluation as well as theoretical analysis of the approach show the efficiency and power of FGEDA in the optimization of functions with complex interactions. It is showed experimentally that FGEDA outperform other well- known EDAs. GECCO track: estimation of distribution Algorithm.

关键词： Algorithm Design Experimentation Performance Optimization problems estimation of distribution algorithms Factor graph Linkage learning Mutual information Matrix factorization

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