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Initial breeding value prediction on Manchego sheep by using rule-based systems

EXPERT SYSTEMS WITH APPLICATIONS 2007年第1期33卷 96-109页

作者： delaOssa, Luis Flores, M. Julia Gamez, Jose A. Mateo, Juan L. Puerta, Jose M. Univ Castilla La Mancha SIMD Albacete 02071 Spain

In this paper we present an application of rule-based expert systems to a farming problem. Concretely the prediction of the breeding value in Manchego ewes is studied for the early stage of their life in which the standard (BLUP) methodology cannot be applied. In this case the pedigree index (arithmetical mean between parents' breeding value) is used to make the estimation. An alternative to this method is presented here, which is based on the use of two different types of rule-based systems: regression rules and linguistic fuzzy rules. The approach proposed is data-driven in the sense that the rules are learnt from data. The results obtained show that the learnt systems are more accurate than the pedigree index, especially for the regression rules case. On the other hand, the linguistic fuzzy rules systems are more easier to understand for human experts, and this is a point to be take into account because of the nature of the problem we are dealing with. (c) 2006 Elsevier Ltd. All rights reserved.

关键词： rule based expert systems regression trees linguistic fuzzy rules breeding value estimation fuzzy rule learning estimation of distribution algorithms

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A NOVEL MULTI-OBJECTIVE estimation OF distribution ALGORITHM BASED ON SENSITIVITY OF OBJECTIVE FUNCTION

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2013年第6期9卷 2543-2566页

作者： Seo, Hyunshik Lee, Chaewoo Ajou Univ Dept Elect & Comp Engn San 5 Suwon 443749 South Korea

Most multi-objective optimization problems (MOPs) have a set of optimal trade-off solutions known as the Pareto-optimal solutions since the objectives in MOPs are usually in conflict with one another. Recently proposed estimation of distribution algorithms (EDAs) build a probability distribution model based on the probabilistic information about decision variables of solutions, and then produce new solutions from the model. In the algorithms, the modeling technique enables the initial large search space to be reduced to small promising solution space during the search. However, the existing EDAs might be inefficient at generating the promising solutions since they depend on the information extracted from the decision variables of current solutions expected to approach the optimal solutions. For effective modeling of the promising solutions, we firstly introduce new information about the relationship between decision variables and objective functions;this information is called sensitivity of objective function. Secondly, we propose a multi-objective estimation of distribution algorithm based on the sensitivity of objective function (MOEDA-S). In the MOEDA-S, the sensitivity-based distribution modeling adapts to the current search strategy such that the convergence-focused search at the beginning part of the search is changed to a diversity-focused search at the latter part of the search. MOEDA-S is compared with two other leading multi-objective evolutionary algorithms on a set of test instances. The simulation results show that MOEDA-S outperforms the two compared algorithms in terms of both convergence and diversity performances of the solutions.

关键词： Multi-objective optimization problems Multi-objective evolutionary algorithms estimation of distribution algorithms Variables sensitivity-based modeling

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Effective Linkage Learning Using Low-Order Statistics and Clustering

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第6期13卷 1233-1246页

作者： Emmendorfer, Leonardo Ramos Ramirez Pozo, Aurora Trinidad Fed Univ Rio Grande Ctr Ciencias Computac BR-96201900 Rio Grande RS Brazil Univ Fed Parana Dept Comp Sci & Numer Methods Engn BR-81531990 Curitiba Parana Brazil

The adoption of probabilistic models for selected individuals is a powerful approach for evolutionary computation. Probabilistic models based on high-order statistics have been used by estimation of distribution algorithms (EDAs), resulting better effectiveness when searching for global optima for hard optimization problems. This paper proposes a new framework for evolutionary algorithms, which combines a simple EDA based on order 1 statistics and a clustering technique in order to avoid the high computational cost required by higher order EDAs. The algorithm uses clustering to group genotypically similar solutions, relying that different clusters focus on different substructures and the combination of information from different clusters effectively combines substructures. The combination mechanism uses an information gain measure when deciding which cluster is more informative for any given gene position, during a pairwise cluster combination. Empirical evaluations effectively cover a comprehensive range of benchmark optimization problems.

关键词： Combinatorial optimization estimation of distribution algorithms evolutionary computation genetic algorithms linkage schema theorem

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Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2013年第3期58卷 195-202页

作者： Armananzas, Ruben Bielza, Concha Chaudhuri, Kallol Ray Martinez-Martin, Pablo Larranaga, Pedro Univ Politecn Madrid Dept Inteligencia Artificial Computat Intelligence Grp Boadilla Del Monte 28660 Spain Kings Coll Hosp London Natl Parkinson Fdn Ctr Excellence London SE5 9RS England Inst Salud Carlos III Ctr Alzheimer Fdn Reina Sofia Unidad Invest Proyecto Alzheimer Madrid 28031 Spain Inst Salud Carlos III Ctr Alzheimer Fdn Reina Sofia CIBERNED Madrid 28031 Spain

Objective: Is it possible to predict the severity staging of a Parkinson's disease (PD) patient using scores of non-motor symptoms? This is the kickoff question for a machine learning approach to classify two widely known PD severity indexes using individual tests from a broad set of non-motor PD clinical scales only. Methods: The Hoehn & Yahr index and clinical impression of severity index are global measures of PD severity. They constitute the labels to be assigned in two supervised classification problems using only non-motor symptom tests as predictor variables. Such predictors come from a wide range of PD symptoms, such as cognitive impairment, psychiatric complications, autonomic dysfunction or sleep disturbance. The classification was coupled with a feature subset selection task using an advanced evolutionary algorithm, namely an estimation of distribution algorithm. Results: Results show how five different classification paradigms using a wrapper feature selection scheme are capable of predicting each of the class variables with estimated accuracy in the range of 72-92%. In addition, classification into the main three severity categories (Mild, moderate and severe) was split into dichotomic problems where binary classifiers perform better and select different subsets of non-motor symptoms. The number of jointly selected symptoms throughout the whole process was low, suggesting a link between the selected non-motor symptoms and the general severity of the disease. Conclusion: Quantitative results are discussed from a medical point of view, reflecting a clear translation to the clinical manifestations of PD. Moreover, results include a brief panel of non-motor symptoms that could help clinical practitioners to identify patients who are at different stages of the disease from a limited set of symptoms, such as hallucinations, fainting, inability to control body sphincters or believing in unlikely facts. (c) 2013 Elsevier B.V. All rights reserved.

关键词： estimation of distribution algorithms Feature subset selection Severity indexes Parkinson's disease

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Problem of Domain/Building Block Preservation in the Evolution of Biological Macromolecules and Evolutionary Computation

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第2期20卷 1345-1362页

作者： Spirov, Alexander V. Myasnikova, Ekaterina M. Russian Acad Sci IM Sechenov Inst Evolutionary Physiol & Biochem St Petersburg 194223 Russia Peter Great St Petersburg Polytech Univ St Petersburg 195251 Russia

Structurally and functionally isolated domains in biological macromolecular evolution, both natural and artificial, are largely similar to "schemata", building blocks (BBs), in evolutionary computation (EC). The problem of preserving in subsequent evolutionary searches the already found domains / BBs is well known and quite relevant in biology as well as in EC. Both biology and EC are seeing parallel and independent development of several approaches to identifying and preserving previously identified domains / BBs. First, we notice the similarity of DNA shuffling methods in synthetic biology and multi-parent recombination algorithms in EC. Furthermore, approaches to computer identification of domains in proteins that are being developed in biology can be aligned with BB identification methods in EC. Finally, approaches to chimeric protein libraries optimization in biology can be compared to evolutionary search methods based on probabilistic models in EC. We propose to validate the prospects of mutual exchange of ideas and transfer of algorithms and approaches between evolutionary systems biology and EC in these three principal directions. A crucial aim of this transfer is the design of new advanced experimental techniques capable of solving more complex problems of in vitro evolution.

关键词： Proteins Biology Evolution (biology) Statistics Sociology Genetic algorithms Synthetic biology Evolutionary computation genetic algorithms synthetic biology evolution of biological macromolecules In Vitro evolution cross disciplinary knowledge transfer transfer of algorithms modularity building blocks conservative protein domains DNA shuffling multi-parent recombination building block identification chimeric protein libraries optimization estimation of distribution algorithms

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An overview of population-based algorithms for multi-objective optimisation

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2015年第9期46卷 1572-1599页

作者： Giagkiozis, Ioannis Purshouse, Robin C. Fleming, Peter J. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided.

关键词： ant colony optimisation artificial immune systems differential evolution particle swarm optimisation genetic algorithms estimation of distribution algorithms

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Analyzing Probabilistic Models in Hierarchical BOA

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2009年第6期13卷 1199-1217页

作者： Hauschild, Mark Pelikan, Martin Sastry, Kumara Lima, Claudio Univ Missouri MEDAL Dept Math & Comp Sci St Louis MO 63121 USA Univ Illinois Dept Ind & Enterprise Syst Engn Illinois Genet Algorithms Lab Urbana IL 61801 USA Univ Algarve Dept Elect & Comp Sci Engn Informat Lab P-8000117 Faro Portugal

The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on four important classes of test problems: concatenated traps, random additively decomposable problems, hierarchical traps and two-dimensional Ising spin glasses with periodic boundary conditions. We argue that although the probabilistic models in hBOA can encode complex probability distributions, analyzing these models is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in consequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.

关键词： estimation of distribution algorithms hierarchical BOA model structure model complexity probabilistic model

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Parallel EDA to create multivariate calibration models for quantitative chemical applications

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2006年第8期66卷 1002-1013页

作者： Mendiburu, A. Miguel-Alonso, J. Lozano, J. A. Ostra, M. Ubide, C. Univ Basque Country Dept Comp Architecture & Technol San Sebastian 20018 Gipuzkoa Spain Univ Basque Country Dept Comp Sci & Artificial Intelligence San Sebastian Spain Univ Basque Country Dept Appl Chem San Sebastian Spain

This paper describes the application of a collection of data mining methods to solve a calibration problem in a quantitative chemistry environment. Experimental data obtained from reactions which involve known concentrations of two or more components are used to calibrate a model that, later, will be used to predict the (unknown) concentrations of those components in a new reaction. This problem can be seen as a selection + prediction one, where the goal is to obtain good values for the variables to predict while minimizing the number of the input variables needed, taking a small subset of really significant ones. Initial approaches to the problem were principal components analysis and filtering combined with two prediction techniques: artificial neural networks and partial least squares regression. Finally, a parallel estimation of distribution algorithm was used to reduce the number of variables to be used for prediction, yielding the best models for all the considered problems. (C) 2006 Elsevier Inc. All rights reserved.

关键词： evolutionary algorithms estimation of distribution algorithms parallel computing artificial neural network partial least squares regression feature extraction and construction chemical calibration models

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Multi-objective evolutionary optimization of subsonic airfoils by meta-modelling and evolution control

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2007年第G5期221卷 805-814页

作者： D'Angelo, S. Minisci, E. Politecn Torino Dept Aeronaut & Space Engn I-10129 Turin Italy

The current work concerns the application of multi-objective evolutionary optimization by approximation function to aerodynamic design. A new general technique, named evolution control (EC), is used in order to manage the on-line enriching of correct solutions database, which is the basis of the learning procedure for the approximators. Substantially, this approach provides that the database, initially quite small and enabling a very inaccurate approximation, should be integrated during the optimization. Such integration is done by means of some choice criteria, allowing deciding which individuals of the current population should be verified. The technique showed being efficacious and very efficient for the considered problem, whose dimensionality are 5. Even if general principle of EC is valid independently from the kind of adopted approximator, this last strongly affects the application. Obtained results are utilized to show how the adoption of artificial neural networks and kriging can differently influence the whole optimization process. Moreover, first results, achieved after reformulating the same problem with seven parameters, support the idea of the performance of the method scale well with dimensionality.

关键词： evolutionary algorithms estimation of distribution algorithms multi-objective optimization evolution control meta-modelling aerodynamic optimization

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Population-based continuous optimization, probabilistic modelling and mean shift

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EVOLUTIONARY COMPUTATION 2005年第1期13卷 29-42页

作者： Gallagher, M Frean, M Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Univ Victoria Sch Math & Comp Sci Wellington New Zealand

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

关键词： probabilistic modelling estimation of distribution algorithms population-based incremental learning mean shift continuous optimization

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