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6th International Workshop on Artificial Intelligence and Pattern Recognition (IWAIPR)

作者： Madera, Julio Ochoa, Alberto Univ Camaguey Dept Comp Sci Camaguey Cuba Inst Cibernet Matemat & Fis Havana Cuba

ISBN: (纸本)9783030011321;9783030011314

In this paper we evaluate a new estimation of distribution Algorithm (EDA) constructed on top of a very successful Bayesian network learning procedure, Max-Min Hill-Climbing (MMHC). The aim of this paper is to check whether the excellent properties reported for this algorithm in machine learning papers, have some impact on the efficiency and efficacy of EDA based optimization. Our experiments show that the proposed algorithm outperform wellknown state of the art EDA like BOA and EBNA in a test bed based on B-functions. On the basis of these results we conclude that the proposed scheme is a promising candidate for challenging real-world applications, specifically, problems related to the areas of Data Mining, Patter Recognition and Artificial Intelligence.

关键词： estimation of distribution algorithms B-functions Bayesian networks Dependency learning Evolutionary optimization

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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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A Modified Screening estimation of distribution Algorithm for Large-Scale Continuous Optimization

A Modified Screening Estimation of Distribution Algorithm fo...

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10th International Conference on Simulated Evolution and Learning (SEAL)

作者： Mishra, Krishna Manjari Gallagher, Marcus Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia

ISBN: (纸本)9783319135632;9783319135625

Continuous estimation of distribution algorithms (EDAs) commonly use a Gaussian distribution to control the search process. For high-dimensional optimization problems, several practical issues arise when estimating a large covariance matrix from the selected population. Recent work in continuous EDAs has aimed to address these issues. The Screening estimation of distribution Algorithm (sEDA) is one such algorithm which, uniquely, utilizes the objective function values obtained during the search. A sensitivity analysis technique is then used to reduce the rank of the covariance matrix, according to the estimated sensitivity of the fitness function to individual variables in the search space. In this paper we analyze sEDA and find that it does not scale well to very high-dimensional problems because it uses a large number of additional fitness function evaluations per generation. A modified version of the algorithm, named sEDA-lite is proposed which requires no additional fitness evaluations for sensitivity analysis. Experiments on a variety of artificial and real-world representative problems evaluate the performance of the algorithm compared with sEDA and EDA-MCC, a related, recently proposed algorithm.

关键词： estimation of distribution algorithms High-Dimensional Optimization problems Screening Technique

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Symmetric-Approximation Energy-Based estimation of distribution (SEED): A Continuous Optimization Algorithm

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IEEE ACCESS 2019年 7卷 154859-154871页

作者： De Anda-Suarez, Juan Martin Carpio-Valadez, Juan Puga-Soberanes, Hector J. Calzada-Ledesma, Valentin Rojas-Dominguez, Alfonso Jeyakumar, Solai Espinal, Andres Inst Tecnol Leon Tecnol Nacl Mexico Leon 37290 Mexico Inst Tecnol Super Purisima del Rincon Tecnol Nacl Mexico Purisima Del Rincon 36425 Mexico Univ Guanajuato Dept Astron Guanajuato 36000 Mexico Univ Guanajuato Dept Estudios Org Guanajuato 36000 Mexico

estimation of distribution algorithms (EDAs) maintain and iteratively update a probabilistic model to tackle optimization problems. The Boltzmann Probability distribution Function (Boltzmann-PDF) provides advantages when used in energy based EDAs. However, direct sampling from the Boltzmann-PDF to update the probabilistic model is unpractical, and several EDAs employ an approximation to the Boltzmann-PDF by means of a Gaussian distribution that is usually derived by the minimization of the Kullback-Leibler divergence (KL-divergence) computed between the Gaussian and the Boltzmann-PDFs. The KL-divergence measure is not symmetric, and this causes the Gaussian approximation to fail at correctly modeling the target function for the EDAs, because the parameters of the Gaussian are not optimally estimated. In this paper, we derive an approximation to the Boltzmann-PDF using Jeffreys' divergence (a symmetric measure) in lieu of the KL-divergence and thus improve the performance of the optimization algorithm. Our approach is termed Symmetric-approximation Energy-based estimation of distribution (SEED) algorithm. The SEED algorithm is experimentally compared under a univariate approach against two other EDAs (UMDAc and BUMDA) on several benchmark optimization problems. The results show that the SEED algorithm is more effective and more efficient than the other algorithms.

关键词： Boltzmann selection estimation of distribution algorithms Kullback-Leibler divergence J-divergence

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FEDA-NRP: A fixed-structure multivariate estimation of distribution algorithm to solve the multi-objective Next Release Problem with requirements interactions

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期124卷

作者： Perez-Piqueras, Victor Bermejo, Pablo Gamez, Jose A. Univ Castilla La Mancha Escuela Super Ingn Informat S N Albacete 02071 Spain

In the development of a software product, the Next Release Problem is the selection of the most appropriate subset of requirements (tasks) to include in the next release of the product, such that the selected subset maximises the overall satisfaction of the stakeholders and minimises the total cost. Furthermore, in most cases, requirements or tasks cannot be developed independently, as there are dependencies between them, which must be respected in the selection for the next release. In this paper, we approach the Next Release Problem as a constrained bi-objective optimisation problem. The main contribution is the design of an estimation of distribution Algorithm that exploits domain knowledge, i.e. the dependencies between the requirements, to define the structure of a Bayesian network that models the relationships between the binary variables (requirements) to be optimised. The use of a Bayesian network with a fixed structure reduces the complexity of the search process, since it is unnecessary to learn the structure at each iteration of the algorithm. Moreover, it ensures that the sampled individuals are always valid with respect to the required dependencies. The second main contribution is the generation of a corpus of synthetic datasets with cost estimations derived from agile and classic management methodologies. Standard multi-objective metrics are computed in order to assess our proposal and compare it with other evolutionary multi-criterion optimisation algorithms, determining that it is the optimal choice when dealing with complex datasets.

关键词： Next Release problem estimation of distribution algorithms Evolutionary multi-objective search Search-based software engineering Bayesian networks Agile

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Structure-control design of a mechatronic system with parallelogram mechanism using an estimation of distribution algorithm

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MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES 2016年第1-2期44卷 58-71页

作者： Ivvan Valdez, S. Chavez-Conde, E. Hernandez, Eusebio E. Ceccarelli, M. Ctr Res Math CIMAT Dept Comp Sci Guanajuato Mexico Univ Papaloapan Campus Loma Bonita Loma Bonita Mexico ESIME UPT Natl Polytech Inst Sect Grad Studies & Res Av Ticoman Mexico City 07340 DF Mexico Univ Cassino & Southern Lazio Lab Robot & Mechatron Cassino Italy

In this paper, a structure-control design methodology for simultaneously optimizing both mechanical structure and control of a parallelogram linkage robot is proposed. It takes into count the dynamical model and the mechanical parameters for the optimization process along with the controller. Thus, proportional-integral-derivative (PID) control and geometric variables are optimized in a simultaneously way. Through the concurrent procedure an optimal combination of the robot structure and controller gains is obtained. The global optimization problem is tackled by using an estimation of distribution algorithm (EDA) based on the Boltzmann distribution. The EDA seeks for the global optimum by estimating and sampling a probability distribution. The proposed methodology is verified through simulation experiments and applied to the design process of a parallelogram linkage system. The results obtained in experiments show the effectiveness of the proposal. This approach is generic and could be applied to other mechanisms in similar way when for concurrent process both kinematic and dynamic models are available along with the controller. In particular, the results are promising when the optimization parameters are uncorrelated, namely control and mechanical parameters.

关键词： Concurrent design estimation of distribution algorithms optimization parallelogram linkage

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An estimation of distribution algorithm based on interactions between requirements to solve the bi-objective Next Release Problem

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JOURNAL OF SYSTEMS AND SOFTWARE 2023年 199卷

作者： del Sagrado, Jose Sierra Ibanez, Jose Antonio del Aguila, Isabel M. Univ Almeria Dept Informat Crtra Sacramento S-N La Canada De San Urbano 04120 Almeria Spain

Selecting the appropriate requirements to develop in the next release of an open market software product under evolution, is a compulsory step of each software development project. This selection should be done by maximizing stakeholders' satisfaction and minimizing development costs, while keeping constraints. In this work we investigate what is the requirements interactions impact when searching for solutions of the bi-objective Next Release Problem. In one hand, these interactions are explicitly included in two algorithms: a branch and bound algorithm and an estimation of distribution algorithm (EDA). And on the other, we study the performance of these not previously used solving approaches by applying them in several instances of small, medium and large size data sets. We find that interactions inclusion do enhance the search and when time restrictions exists, as in the case of the bi-objective Next Release Problem, EDAs have proven to be stable and reliable locating a large number of solutions on the reference Pareto front.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Software requirements Next release problem estimation of distribution algorithms Requirements interactions

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A HYBRID estimation OF distribution ALGORITHM FOR CDMA CELLULAR SYSTEM DESIGN

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2008年第2期7卷 187-200页

作者： Sun, Jianyong Zhang, Qingfu Li, Jin Yao, Xin Univ Birmingham Sch Comp Sci Cercia Birmingham B15 2TT W Midlands England Univ Essex Dept Comp Sci Colchester CO4 3SQ Essex England

This paper proposes a hybrid estimation of distribution algorithm (HyEDA) to address the design problem of code division multiple access cellular system configuration. Given a service area, the problem is to find a set of optimal locations of base stations, associated with their corresponding powers and antenna heights in the area, in order to maximize call quality and service coverage, at the same time, to minimize the total cost of the system configuration. HyEDA is a two-stage hybrid approach which integrates an estimation of distribution algorithm, a K-means clustering method, and a simple local search algorithm. We have compared HyEDA with a simulated annealing method on a number of instances. Our simulation results have demonstrated that HyEDA outperforms the simulated annealing method in terms of the solution quality and computational cost.

关键词： CDMA cellular system configuration design hybrid evolutionary algorithm estimation of distribution algorithms

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Bias Reduction of Probabilistic Prototype Tree based estimation of distribution Genetic Programming in Predicting Arthritis Prevalence

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Procedia Computer Science 2013年 24卷 121-125页

作者： Kangil Kim Hanggjun Cho Seoul National University Structural Complexity Laboratory Building 302 Gwanangno 599 Seoul 151–744 Korea

estimation of distribution algorithms in Genetic Programming (EDA-GP) are algorithms applying stochastic model learning to genetic programming. In spite of various potential benefits, probabilistic prototype tree (PPT) based EDA-GPs recently appeared to have a critical problem of losing diversity easily. As an alternative learning method to reduce the effect, likelihood weighting (LW) was proposed and its results were positive to improve EDA-GP performance. In this paper, we aim to provide more generalised verification results to confirm the effects of LW. We investigate performance of PPT-based EDA-GP in a large scale problem predicting arthritis using medical data.

关键词： estimation of distribution algorithms Genetic Programming Probabilistic Prototype Tree Arthritis Disease Prediction Bias

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Saving computational budget in Bayesian network-based evolutionary algorithms

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NATURAL COMPUTING 2021年第4期20卷 775-790页

作者： Scoczynski, Marcella Delgado, Myriam Luders, Ricardo Oliva, Diego Wagner, Markus Sung, Inkyung El Yafrani, Mohamed Fed Univ Technol Parana UTFPR Curitiba Parana Brazil Univ Oberta Catalunya IN3 Comp Sci Dept Barcelona Spain Univ Guadalajara Dept Ciencias Computac CUCEI Guadalajara Jalisco Mexico Univ Adelaide Optimisat & Logist Adelaide SA Australia Aalborg Univ Operat Res Grp Aalborg Denmark

During the evolutionary process, algorithms based on probability distributions for generating new individuals suffer from computational burden due to the intensive computation of probability distribution estimations, particularly when using Probabilistic Graph Models (PGMs). In the Bayesian Optimisation Algorithm (BOA), for instance, determining the optimal Bayesian network structure by a given solution sample is an NP-hard problem. To overcome this issue, we consider a new BOA-based optimisation approach (FBOA) which explores the fact that patterns of PGM adjustments can be used as a guide to reduce the frequency of PGM updates because significant changes in PGM structure might not occur so frequently, and because they can be particularly sparse at the end of evolution. In the present paper, this new approach is scrutinised in the search space of an NK-landscape optimisation problem for medium and large-size instances. Average gaps and success rates as well as the correlation between the landscape ruggedness of the problem and the expected runtime of FBOA and BOA are presented for medium-size instances. For large-size instances, optimisation results from FBOA and BOA are compared. The experiments show that, despite our FBOA being of almost three times faster than BOA, it still produces competitive optimisation results.

关键词： estimation of distribution algorithms Probabilistic graphical models Bayesian networks

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