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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Doerr, Benjamin Dufay, Marc Inst Polytech Paris Ecole Polytech CNRS LIX Palaiseau France Inst Polytech Paris Ecole Polytech Palaiseau France

ISBN: (纸本)9783031147210;9783031147203

We propose a general formulation of a univariate estimation-of-distribution algorithm (EDA). It naturally incorporates the three classic univariate EDAs compact genetic algorithm, univariate marginal distribution algorithm and population-based incremental learning as well as the max-min ant system with iteration-best update. Our unified description of the existing algorithms allows a unified analysis of these;we demonstrate this by providing an analysis of genetic drift that immediately gives the existing results proven separately for the four algorithms named above. Our general model also includes EDAs that are more efficient than the existing ones and these may not be difficult to find as we demonstrate for the ONEMAX and LEADINGONES benchmarks.

关键词： estimation of distribution algorithms Genetic drift Running time analysis Theory

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Polarity Prediction in Tourism Cuban Reviews Using Transformer with estimation of distribution algorithms 8th

Polarity Prediction in Tourism Cuban Reviews Using Transfor...

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8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023

作者： Toledano-López, Orlando Grabiel Álvarez-Carmona, Miguel Ángel Madera, Julio Simón-Cuevas, Alfredo López-Rodríguez, Yoan Antonio González Diéz, Héctor Habana Cuba Centro de Investigacion en Matemáticas Sede Monterrey Mexico Universidad de Camagüey Camagüey Cuba Universidad Tecnológica de La Habana "José Antonio Echeverría" Habana Cuba

ISBN: (纸本)9783031495519

The tourism sector has benefited from recent research in the area of natural language processing, where digital platforms on the web offer the opportunity for people to express their opinions about the services and places they visit. The texts of the reviews are unstructured data characterized by high dimensionality, variable size, and complex semantic relationships between words, which has led to the development of neural architectures with a larger number of parameters to optimize. The training of deep neural networks has been approached by methods based on partial derivatives of the objective function and presents several theoretical and practical limitations, such as the probability of convergence to local minima. In this paper, a hybrid method based on distribution estimation algorithms is proposed for fine-tuning an mT5-based Transformer for polarity prediction. For this purpose, a new Spanish dataset is proposed for polarity classification compiled from TripAdvisor reviews of Cuba. Different preprocessing variants are applied and compared in the solution and data imbalance is treated by back translation. The proposed method combined with back translation decreases the mean of the absolute error in the mT5-based Transformer for polarity prediction. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： estimation of distribution algorithms mT5-based Transformer Polarity prediction

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estimation of particle swarm distribution algorithms: Combining the benefits of PSO and EDAs

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INFORMATION SCIENCES 2012年 192卷 109-119页

作者： Ahn, Chang Wook An, Jinung Yoo, Jae-Chern Sungkyunkwan Univ Sch Informat & Commun Engn Seoul South Korea

This paper presents a novel framework of the estimation of particle swarm distribution algorithms (EPSDAs). The aim is to effectively combine particle swarm optimization (PSO) with the estimation of distribution algorithms (EDAs) without losing their unique features. This aim is achieved by incorporating the following mechanisms: (1) selection is applied to the local best solutions in order to obtain more promising individuals for model building, (2) a probabilistic model of the problem is built from the selected solutions, and (3) new individuals are generated by a stochastic combination of the EDA's model sampling method and the PSO's particle moving mechanism. To exhibit the utility of the EPSDA framework, an extended compact particle swarm optimization (EcPSO) is developed by combining the strengths of the extended compact genetic algorithm (EcGA) with binary PSO (BPSO), along the lines of the suggested framework. Due to its effective nature of harmonizing the global search of EcGA with the local search of BPSO, EcPSO is able to discover the optimal solution in a fast and reliable manner. Experimental results on artificial to real-world problems have adduced grounds for the effectiveness of the proposed approach. (C) 2010 Elsevier Inc. All rights reserved.

关键词： estimation of distribution algorithms Particle swarm optimization Extended compact genetic algorithm Local search Global search Probabilistic model building

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AutoClustering An estimation of distribution Algorithm for the Automatic Generation of Clustering algorithms

AutoClustering An Estimation of Distribution Algorithm for t...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Meiguins, Aruanda S. G. Limao, Roberto C. Meiguins, Bianchi S. Junior, Samuel F. S. Freitas, Alex A. UFPA PPGEE Belem Para Brazil ICEN UFPA Belem Para Brazil Univ Kent Canterbury Kent England

ISBN: (纸本)9781467315098

Most of the existing Data Mining algorithms have been manually produced, that is, have been developed by a human programmer. A prominent Artificial Intelligence research area is automatic programming - the generation of a computer program by another computer program. Clustering is an important data mining task with many useful real-world applications. Particularly, the class of clustering algorithms based on the idea of data density to identify clusters has many advantages, such as the ability to identify arbitrary-shape clusters. We propose the use of estimation of distribution algorithms for the artificial generation of density-based clustering algorithms. In order to guarantee the generation of valid algorithms, a directed acyclic graph (DAG) was defined where each node represents a procedure (building block) and each edge represents a possible execution sequence between two nodes. The Building Blocks DAG specifies the alphabet of the EDA, that is, any possibly generated algorithm. Preliminary experimental results compare the clustering algorithms artificially generated by AutoClustering to DBSCAN, a well-known manually-designed algorithm.

关键词： Programming Density-Based Clustering estimation of distribution algorithms Data Mining

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Optimising cancer chemotherapy using an estimation of distribution algorithm and genetic algorithms 06

Optimising cancer chemotherapy using an estimation of distri...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Petrovski, Andrei Shakya, Siddhartha McCall, John Robert Gordon Univ Sch Comp Aberdeen Scotland

ISBN: (纸本)9781595931863

This paper presents a methodology for using heuristic search methods to optimise cancer chemotherapy. Specifically, two evolutionary algorithms - Population Based Incremental Learning (PBIL), which is an estimation of distribution Algorithm (EDA), and Genetic algorithms (GAs) have been applied to the problem of finding effective chemotherapeutic treatments. To our knowledge, EDAs have been applied to fewer real world problems compared to GAs, and the aim of the present paper is to expand the application domain of this technique. We compare and analyse the performance of both algorithms and draw a conclusion as to which approach to cancer chemotherapy optimisation is more efficient and helpful in the decision-making activity led by the oncologists.

关键词： estimation of distribution algorithms evolutionary computation probabilistic modelling

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Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks

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EVOLUTIONARY COMPUTATION 2005年第1期13卷 43-66页

作者： Peña, JM Lozano, JA Larrañaga, P Linkoping Univ Dept Phys & Measurement Technol Linkoping Sweden Univ Basque Country Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Bilbao Spain

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

关键词： estimation of distribution algorithms Bayesian networks unsupervised learning

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A Distance-Based Ranking Model estimation of distribution Algorithm for the Flowshop Scheduling Problem

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2014年第2期18卷 286-300页

作者： Ceberio, Josu Irurozki, Ekhine Mendiburu, Alexander Lozano, Jose A. Univ Basque Country UPV EHU Intelligent Syst Grp Dept Comp Sci & Artificial Intelligence Gipuzkoa 20018 Spain

The aim of this paper is two-fold. First, we introduce a novel general estimation of distribution algorithm to deal with permutation-based optimization problems. The algorithm is based on the use of a probabilistic model for permutations called the generalized Mallows model. In order to prove the potential of the proposed algorithm, our second aim is to solve the permutation flowshop scheduling problem. A hybrid approach consisting of the new estimation of distribution algorithm and a variable neighborhood search is proposed. Conducted experiments demonstrate that the proposed algorithm is able to outperform the state-of-the-art approaches. Moreover, from the 220 benchmark instances tested, the proposed hybrid approach obtains new best known results in 152 cases. An in-depth study of the results suggests that the successful performance of the introduced approach is due to the ability of the generalized Mallows estimation of distribution algorithm to discover promising regions in the search space.

关键词： estimation of distribution algorithms generalized Mallows model permutation flowshop scheduling problem permutations-based optimization problems

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A block based estimation of distribution algorithm using bivariate model for scheduling problems

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SOFT COMPUTING 2014年第6期18卷 1177-1188页

作者： Chang, Pei-Chann Chen, Meng-Hui Yuan Ze Univ Dept Informat Management Taoyuan 32026 Taiwan

Recently, estimation of distribution algorithms (EDAs) have gradually attracted a lot of attention and have emerged as a prominent alternative to traditional evolutionary algorithms. In this paper, a block-based EDA using bivariate model is developed to solve combinatorial problems. Instead of generating a set of chromosomes, our approach generates a set of promising blocks using bivariate model and these blocks are reserved in an archive for future use. These blocks will be updated every other k generation. Then, two rules, i.e., AC1 and AC2, are developed to generate a new chromosome by combining the set of selected blocks and rest of genes. This block based approach is very efficient and effective when compared with the traditional EDAs. According to the experimental results, the block based EDA outperforms EDA, GA, ACO and other evolutionary approaches in solving benchmark permutation problems. The block based approach is a new concept and has a very promising result for other applications.

关键词： Combinatorial problems estimation of distribution algorithms Bivariate probabilistic model Artificial chromosomes

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Analysis of Bayesian Network Learning Techniques for a Hybrid Multi-objective Bayesian estimation of distribution Algorithm: a case study on MNK Landscape

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JOURNAL OF HEURISTICS 2021年第4期27卷 549-573页

作者： Martins, Marcella S. R. Yafrani, Mohamed El Delgado, Myriam Luders, Ricardo Santana, Roberto Siqueira, Hugo V. Akcay, Huseyin G. Ahiod, Belaid Fed Univ Technol Parana UTFPR Curitiba Parana Brazil Aalborg Univ Operat Res Grp Aalborg Denmark Univ Basque Country UPV EHU San Sebastian Spain Akdeniz Univ AKU Antalya Turkey Mohammed V Univ Rabat Rabat Morocco

This work investigates different Bayesian network structure learning techniques by thoroughly studying several variants ofHybrid Multi-objectiveBayesian estimation of distribution Algorithm (HMOBEDA), applied to the MNK Landscape combinatorial problem. In the experiments, we evaluate the performance considering three different aspects: optimization abilities, robustness and learning efficiency. Results for instances of multi- and many-objective MNK-landscape show that, score-based structure learning algorithms appear to be the best choice. In particular, HMOBEDA(k2) was capable of producing results comparable with the other variants in terms of the runtime of convergence and the coverage of the final Pareto front, with the additional advantage of providing solutions that are less sensible to noise while the variability of the corresponding Bayesian network models is reduced.

关键词： Many-objective optimization estimation of distribution algorithms Structure learning techniques Robustness

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MARLEDA: Effective distribution estimation through Markov random fields

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THEORETICAL COMPUTER SCIENCE 2016年 633卷 4-18页

作者： Alden, Matthew Miikkulainen, Risto Univ Washington Inst Technol Tacoma WA 98402 USA Univ Texas Austin Dept Comp Sci Austin TX 78741 USA

estimation of distribution algorithms (EDAs) combine genetic algorithms with statistical modeling in order to learn and exploit the structure of search domains. Such algorithms work well when the EDA's statistical model matches the structure of the domain. Many EDAs use statistical models that represent domain structure with directed acyclic graphs (DAGs). While useful in many areas, DAGs have inherent restrictions that make undirected graph models a viable alternative for many domains. This paper introduces a new EDA, the Markovian Learning estimation of distribution Algorithm (MARLEDA), that makes effective use of this idea by employing a Markov random field model. MARLEDA is evaluated on four combinatorial optimization tasks, OneMax, deceptive trap functions, the 2D Rosenbrock function, and 2D Ising spin glasses. MARLEDA is shown to perform better than standard genetic algorithms and a DAG-based EDA. Improving the modeling capabilities of EDAs in this manner brings them closer to effective applications in difficult real-world domains, such as computational biology and autonomous agent design. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Probabilistic modeling estimation of distribution algorithms Markov random field Markov chain Monte Carlo

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