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Author David Roche edit  openurl
  Title A Statistical Framework for Terminating Evolutionary Algorithms at their Steady State Type Book Whole
  Year (down) 2015 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract As any iterative technique, it is a necessary condition a stop criterion for terminating Evolutionary Algorithms (EA). In the case of optimization methods, the algorithm should stop at the time it has reached a steady state so it can not improve results anymore. Assessing the reliability of termination conditions for EAs is of prime importance. A wrong or weak stop criterion can negatively a ect both the computational e ort and the nal result.
In this Thesis, we introduce a statistical framework for assessing whether a termination condition is able to stop EA at its steady state. In one hand a numeric approximation to steady states to detect the point in which EA population has lost its diversity has been presented for EA termination. This approximation has been applied to di erent EA paradigms based on diversity and a selection of functions covering the properties most relevant for EA convergence. Experiments show that our condition works regardless of the search space dimension and function landscape and Di erential Evolution (DE) arises as the best paradigm. On the other hand, we use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in xspace.
Our theoretical framework is analyzed across several benchmark test functions
and two standard termination criteria based on function improvement in f-space and EA population x-space distribution for the DE paradigm. Results validate our statistical framework as a powerful tool for determining the capability of a measure for terminating EA and select the x-space distribution as the best-suited for accurately stopping DE in real-world applications.		  
  Address July 2015  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Debora Gil;Jesus Giraldo  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 600.075 Approved no  
  Call Number Admin @ si @ Roc2015 Serial 2686  
Permanent link to this record
 
 
Author Debora Gil; David Roche; Agnes Borras; Jesus Giraldo edit  doi
openurl 
  Title Terminating Evolutionary Algorithms at their Steady State Type Journal Article
  Year (down) 2015 Publication Computational Optimization and Applications Abbreviated Journal COA  
  Volume 61 Issue 2 Pages 489-515  
  Keywords Evolutionary algorithms; Termination condition; Steady state; Differential evolution  
  Abstract Assessing the reliability of termination conditions for evolutionary algorithms (EAs) is of prime importance. An erroneous or weak stop criterion can negatively affect both the computational effort and the final result. We introduce a statistical framework for assessing whether a termination condition is able to stop an EA at its steady state, so that its results can not be improved anymore. We use a regression model in order to determine the requirements ensuring that a measure derived from EA evolving population is related to the distance to the optimum in decision variable space. Our framework is analyzed across 24 benchmark test functions and two standard termination criteria based on function fitness value in objective function space and EA population decision variable space distribution for the differential evolution (DE) paradigm. Results validate our framework as a powerful tool for determining the capability of a measure for terminating EA and the results also identify the decision variable space distribution as the best-suited for accurately terminating DE in real-world applications.  
  Address  
  Corporate Author Thesis  
  Publisher Springer US Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0926-6003 ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 600.044; 605.203; 600.060; 600.075 Approved no  
  Call Number Admin @ si @ GRB2015 Serial 2560  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit  doi
isbn  openurl
  Title Mathematical modeling of G protein-coupled receptor function: What can we learn from empirical and mechanistic models? Type Book Chapter
  Year (down) 2014 Publication G Protein-Coupled Receptors – Modeling and Simulation Advances in Experimental Medicine and Biology Abbreviated Journal  
  Volume 796 Issue 3 Pages 159-181  
  Keywords β-arrestin; biased agonism; curve fitting; empirical modeling; evolutionary algorithm; functional selectivity; G protein; GPCR; Hill coefficient; intrinsic efficacy; inverse agonism; mathematical modeling; mechanistic modeling; operational model; parameter optimization; receptor dimer; receptor oligomerization; receptor constitutive activity; signal transduction; two-state model  
  Abstract Empirical and mechanistic models differ in their approaches to the analysis of pharmacological effect. Whereas the parameters of the former are not physical constants those of the latter embody the nature, often complex, of biology. Empirical models are exclusively used for curve fitting, merely to characterize the shape of the E/[A] curves. Mechanistic models, on the contrary, enable the examination of mechanistic hypotheses by parameter simulation. Regretfully, the many parameters that mechanistic models may include can represent a great difficulty for curve fitting, representing, thus, a challenge for computational method development. In the present study some empirical and mechanistic models are shown and the connections, which may appear in a number of cases between them, are analyzed from the curves they yield. It may be concluded that systematic and careful curve shape analysis can be extremely useful for the understanding of receptor function, ligand classification and drug discovery, thus providing a common language for the communication between pharmacologists and medicinal chemists.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Netherlands Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0065-2598 ISBN 978-94-007-7422-3 Medium  
  Area Expedition Conference  
  Notes IAM; 600.075 Approved no  
  Call Number IAM @ iam @ RGG2014 Serial 2197  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit   pdf
doi  isbn
openurl 
  Title Detecting loss of diversity for an efficient termination of EAs Type Conference Article
  Year (down) 2013 Publication 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal  
  Volume Issue Pages 561 - 566  
  Keywords EA termination; EA population diversity; EA steady state  
  Abstract Termination of Evolutionary Algorithms (EA) at its steady state so that useless iterations are not performed is a main point for its efficient application to black-box problems. Many EA algorithms evolve while there is still diversity in their population and, thus, they could be terminated by analyzing the behavior some measures of EA population diversity. This paper presents a numeric approximation to steady states that can be used to detect the moment EA population has lost its diversity for EA termination. Our condition has been applied to 3 EA paradigms based on diversity and a selection of functions
covering the properties most relevant for EA convergence.
Experiments show that our condition works regardless of the search space dimension and function landscape.		  
  Address Timisoara; Rumania;  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-4799-3035-7 Medium  
  Area Expedition Conference SYNASC  
  Notes IAM; 600.044; 600.060; 605.203 Approved no  
  Call Number Admin @ si @ RGG2013c Serial 2299  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit  doi
openurl 
  Title Mechanistic analysis of the function of agonists and allosteric modulators: Reconciling two-state and operational models Type Journal Article
  Year (down) 2013 Publication British Journal of Pharmacology Abbreviated Journal BJP  
  Volume 169 Issue 6 Pages 1189-202  
  Keywords  
  Abstract Two-state and operational models of both agonism and allosterism are compared to identify and characterize common pharmacological parameters. To account for the receptor-dependent basal response, constitutive receptor activity is considered in the operational models. By arranging two-state models as the fraction of active receptors and operational models as the fractional response relative to the maximum effect of the system, a one-by-one correspondence between parameters is found. The comparative analysis allows a better understanding of complex allosteric interactions. In particular, the inclusion of constitutive receptor activity in the operational model of allosterism allows the characterization of modulators able to lower the basal response of the system; that is, allosteric modulators with negative intrinsic efficacy. Theoretical simulations and overall goodness of fit of the models to simulated data suggest that it is feasible to apply the models to experimental data and constitute one step forward in receptor theory formalism.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 600.044; 605.203 Approved no  
  Call Number IAM @ iam @ RGG2013b Serial 2195  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit  url
doi  openurl
  Title Multiple active receptor conformation, agonist efficacy and maximum effect of the system: the conformation-based operational model of agonism, Type Journal Article
  Year (down) 2013 Publication Drug Discovery Today Abbreviated Journal DDT  
  Volume 18 Issue 7-8 Pages 365-371  
  Keywords  
  Abstract The operational model of agonism assumes that the maximum effect a particular receptor system can achieve (the Em parameter) is fixed. Em estimates are above but close to the asymptotic maximum effects of endogenous agonists. The concept of Em is contradicted by superagonists and those positive allosteric modulators that significantly increase the maximum effect of endogenous agonists. An extension of the operational model is proposed that assumes that the Em parameter does not necessarily have a single value for a receptor system but has multiple values associated to multiple active receptor conformations. The model provides a mechanistic link between active receptor conformation and agonist efficacy, which can be useful for the analysis of agonist response under different receptor scenarios.  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes IAM; 600.057; 600.054 Approved no  
  Call Number IAM @ iam @ RGG2013a Serial 2190  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit   pdf
url  openurl
  Title Assessing agonist efficacy in an uncertain Em world Type Conference Article
  Year (down) 2012 Publication 40th Keystone Symposia on mollecular and celular biology Abbreviated Journal  
  Volume Issue Pages 79  
  Keywords  
  Abstract The operational model of agonism has been widely used for the analysis of agonist action since its formulation in 1983. The model includes the Em parameter, which is defined as the maximum response of the system. The methods for Em estimation provide Em values not significantly higher than the maximum responses achieved by full agonists. However, it has been found that that some classes of compounds as, for instance, superagonists and positive allosteric modulators can increase the full agonist maximum response, implying upper limits for Em and thereby posing doubts on the validity of Em estimates. Because of the correlation between Em and operational efficacy, τ, wrong Em estimates will yield wrong τ estimates.
In this presentation, the operational model of agonism and various methods for the simulation of allosteric modulation will be analyzed. Alternatives for curve fitting will be presented and discussed.		  
  Address Fairmont Banff Springs, Banff, Alberta, Canada  
  Corporate Author Keystone Symposia Thesis  
  Publisher Keystone Symposia Place of Publication Editor A. Christopoulus and M. Bouvier  
  Language english Summary Language english Original Title  
  Series Editor Keystone Symposia Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference KSMCB  
  Notes IAM Approved no  
  Call Number IAM @ iam @ RGG2012 Serial 1855  
Permanent link to this record
 
 
Author Aura Hernandez-Sabate; Debora Gil; David Roche; Monica M. S. Matsumoto; Sergio S. Furuie edit   pdf
url  openurl
  Title Inferring the Performance of Medical Imaging Algorithms Type Conference Article
  Year (down) 2011 Publication 14th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 6854 Issue Pages 520-528  
  Keywords Validation, Statistical Inference, Medical Imaging Algorithms.  
  Abstract Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.		  
  Address Sevilla  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Heidelberg Place of Publication Berlin Editor Pedro Real; Daniel Diaz-Pernil; Helena Molina-Abril; Ainhoa Berciano; Walter Kropatsch  
  Language Summary Language Original Title  
  Series Editor Series Title L Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CAIP  
  Notes IAM; ADAS Approved no  
  Call Number IAM @ iam @ HGR2011 Serial 1676  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit   pdf
openurl 
  Title Using statistical inference for designing termination conditions ensuring convergence of Evolutionary Algorithms Type Conference Article
  Year (down) 2011 Publication 11th European Conference on Artificial Life Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract A main challenge in Evolutionary Algorithms (EAs) is determining a termination condition ensuring stabilization close to the optimum in real-world applications. Although for known test functions distribution-based quantities are good candidates (as far as suitable parameters are used), in real-world problems an open question still remains unsolved. How can we estimate an upper-bound for the termination condition value ensuring a given accuracy for the (unknown) EA solution?
We claim that the termination problem would be fully solved if we defined a quantity (depending only on the EA output) behaving like the solution accuracy. The open question would be, then, satisfactorily answered if we had a model relating both quantities, since accuracy could be predicted from the alternative quantity. We present a statistical inference framework addressing two topics: checking the correlation between the two quantities and defining a regression model for predicting (at a given confidence level) accuracy values from the EA output.		  
  Address Paris, France  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ECAL  
  Notes IAM; Approved no  
  Call Number IAM @ iam @ RGG2011b Serial 1678  
Permanent link to this record
 
 
Author David Roche; Debora Gil; Jesus Giraldo edit   pdf
url  isbn
openurl 
  Title An inference model for analyzing termination conditions of Evolutionary Algorithms Type Conference Article
  Year (down) 2011 Publication 14th Congrès Català en Intel·ligencia Artificial Abbreviated Journal  
  Volume Issue Pages 216-225  
  Keywords Evolutionary Computation Convergence, Termination Conditions, Statistical Inference  
  Abstract In real-world problems, it is mandatory to design a termination condition for Evolutionary Algorithms (EAs) ensuring stabilization close to the unknown optimum. Distribution-based quantities are good candidates as far as suitable parameters are used. A main limitation for application to real-world problems is that such parameters strongly depend on the topology of the objective function, as well as, the EA paradigm used.
We claim that the termination problem would be fully solved if we had a model measuring to what extent a distribution-based quantity asymptotically behaves like the solution accuracy. We present a regression-prediction model that relates any two given quantities and reports if they can be statistically swapped as termination conditions. Our framework is applied to two issues. First, exploring if the parameters involved in the computation of distribution-based quantities influence their asymptotic behavior. Second, to what extent existing distribution-based quantities can be asymptotically exchanged for the accuracy of the EA solution.		  
  Address Lleida, Catalonia (Spain)  
  Corporate Author Associació Catalana Intel·ligència Artificial Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-1-60750-841-0 Medium  
  Area Expedition Conference CCIA  
  Notes IAM Approved no  
  Call Number IAM @ iam @ RGG2011a Serial 1677  
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