J. A. Lozano

发表

Routing architecture for guaranted services

Francisco Sandoval Hernández, Alfonso Ariza Quintana, M. C. Oria, 1999 .

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation

J. A. Lozano, Pedro Larraanaga, 2001 .

Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)

J. A. Lozano, Iñaki Inza, P. Larrañaga, 2006 .

Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2002, Genetic Algorithms and Evolutionary Computation.

On the performan e of Estimation of Distribution Algorithmsapplied to Software

J. A. Lozano, TestingRamón Sagarna, 2003 .

Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems

J. A. Lozano, P. Larrañaga, Cristina González, 2000 .

Benefits of Data Clustering in Multimodal Function Optimization via EDAs

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2002, Estimation of Distribution Algorithms.

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2002, Machine Learning.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

José Antonio Lozano, José Miguel-Alonso, Tania Lorido-Botran, 2014, Journal of Grid Computing.

Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers

José Antonio Lozano, Juan Diego Rodríguez, J. A. Lozano, 2008, 2008 Eighth International Conference on Hybrid Intelligent Systems.

The weighted independent domination problem: Integer linear programming models and metaheuristic approaches

Christian Blum, José Antonio Lozano, Pedro Pinacho Davidson, 2018, Eur. J. Oper. Res..

An empirical comparison of four initialization methods for the K-Means algorithm

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Sampling and learning the Mallows and Generalized Mallows models under the Cayley distance

J. A. Lozano, Ekhine Irurozki, Borja Calvo, 2014 .

PerMallows: An R Package for Mallows and Generalized Mallows Models

J. A. Lozano, Ekhine Irurozki, Borja Calvo, 2016 .

Algorithms

J. A. Lozano, P. Gács, Dóra Erdős, 1992 .

Analyzing the k Most Probable Solutions in EDAs Based on Bayesian Networks

J. A. Lozano, Roberto Santana, Carlos Echegoyen, 2010 .

Optimizing the number of classes in automated zooplankton classification

J. A. Lozano, Iñaki Inza, J. A. Fernandes, 2009 .

Properties of Kikuchi approximations constructed from clique based decompositions

J. A. Lozano, P. Larrañaga, R. Santana, 2022 .

Prioritization of candidate cancer genes—an aid to oncogenomic studies

J. A. Lozano, P. Larrañaga, N. López-Bigas, 2008, Nucleic acids research.

Differential Micro RNA Expression in PBMC from Multiple Sclerosis Patients

J. A. Lozano, Iñaki Inza, S. Baranzini, 2009, PloS one.

An empirical analysis of loopy belief propagation in three topologies : grids , small-world networks and random graphs

J. A. Lozano, Roberto Santana, A. Mendiburu, 2008 .

Thesis proposal : A study on the discriminatory capacity of the temporal information on supervised time series classification problems

J. A. Lozano, A. Abanda, 2018 .

A preliminary study on EDAs for permutation problems based on marginal-based models

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2011, GECCO '11.

More than one Author with different Affiliations

J. A. Lozano, Iñaki Inza, J. A. Fernandes, 2019 .

Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2000 .

Parallel EDAs to create multivariate calibration models for quantitative chemical applications

Alexander Mendiburu, José Antonio Lozano, José Miguel-Alonso, 2006, J. Parallel Distributed Comput..

Chapter 2 Machine Learning : An Indispensable Tool in Bioinformatics

J. A. Lozano, Iñaki Inza, P. Larrañaga, 2017 .

The Multivariate Normal Distribution Framework for Analyzing Association Studies

J. A. Lozano, E. Eskin, F. Hormozdiari, 2017, bioRxiv.

The EMPATHIC project: mid-term achievements

M. Inés Torres, Sergio Escalera, Gennaro Cordasco, 2019, PETRA.

Software Metrics Mining to Predict the Performance of Estimation of Distribution Algorithms in Test Data Generation

Jose A. Lozano, Ramón Sagarna, J. A. Lozano, 2008 .

On The Performance Of Estimation Of Distribution Algorithms Applied To Software Testing

José Antonio Lozano, Ramón Sagarna, J. A. Lozano, 2005, Appl. Artif. Intell..

Tutorial 7—Adaptive Business Intelligence - Evolutionary Computation for Real World

Qingfu Zhang, Zbigniew Michalewicz, Riccardo Poli, 2007 .

A general framework for the statistical analysis of the sources of variance for classification error estimators

José Antonio Lozano, Aritz Pérez Martínez, Juan Diego Rodríguez, 2013, Pattern Recognit..

ICES CM 2013 / 3034 Q : 01 Forecasting recruitment of multiple fish species using multi-dimensional Bayesian network classifiers

J. A. Lozano, Iñaki Inza, J. A. Fernandes, 2013 .

Minimax Classification under Concept Drift with Multidimensional Adaptation and Performance Guarantees

J. A. Lozano, S. Mazuelas, Verónica Álvarez, 2022, ICML.

Fitting the data from embryo implantation prediction: Learning from label proportions

Iñaki Inza, Jose A Lozano, Jerónimo Hernández-González, 2018, Statistical methods in medical research.

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

Jose A. Lozano, Iñaki Inza, J. A. Lozano, 2019, Artificial Intelligence Review.

Machine Learning : Editorial

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2005 .

Forecasting recruitment of multiple fish species using multi-dimensional Bayesian network classifiers.

Jose A. Lozano, Iñaki Inza, Juan Diego Rodríguez, 2013 .

A Semi-supervised Approach to Multi-dimensional Classification with Application to Sentiment Analysis

Iñaki Inza, Juan Diego Rodríguez, Jonathan Ortigosa-Hernández, 2010 .

Discriminative Learning of Bayesian Network Classifiers

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2006, Inteligencia Artif..

The Convergence Behavior of the PBIL Algorithm

Pedro Larrañaga, Jose A. Lozano, Cristina González, 2001 .

Genetic algorithms applied to Bayesian networks

Pedro Larrañaga, Jose A. Lozano, Manuel Graña, 1996 .

Estimation of distribution algorithms with affinity propagation methods

Pedro Larrañaga, Roberto Santana, Jose Antonio Lozano, 2008 .

A roadmap for solving optimization problems with estimation of distribution algorithms

J. A. Lozano, Josu Ceberio, A. Mendiburu, 2022, Natural Computing.

Multi-start Methods

Alexander Mendiburu, José Antonio Lozano, Rafael Martí, 2018, Handbook of Heuristics.

Increasing Power of Genome-wide Association Studies by Collecting Additional SNPs

J. A. Lozano, Emrah Kostem, E. Eskin, 2011 .

A review on distance based time series classification

J. A. Lozano, A. Abanda, U. Mori, 2018, Data mining and knowledge discovery.

Analyzing rare event, anomaly, novelty and outlier detection terms under the supervised classification framework

J. A. Lozano, Iñaki Inza, Ander Carreño, 2019, Artificial Intelligence Review.

A review of distances for the Mallows and Generalized Mallows estimation of distribution algorithms

J. A. Lozano, Josu Ceberio, Ekhine Irurozki, 2015, Computational Optimization and Applications.

A Survey on Preserving Fairness Guarantees in Changing Environments

J. A. Lozano, Novi Quadrianto, Paula Gordaliza, 2022, ArXiv.

Dealing with the evaluation of supervised classification algorithms

J. A. Lozano, Iñaki Inza, G. Santafé, 2015, Artificial Intelligence Review.

A Markovianity based optimisation algorithm

Siddhartha Shakya, Roberto Santana, José Antonio Lozano, 2012, Genetic Programming and Evolvable Machines.

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2010, Evolutionary Computation.

Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2006, Towards a New Evolutionary Computation.

Toward Understanding EDAs Based on Bayesian Networks Through a Quantitative Analysis

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2012, IEEE Transactions on Evolutionary Computation.

Estimation of Bayesian networks algorithms in a class of complex networks

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2010, IEEE Congress on Evolutionary Computation.

Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations

J. A. Lozano, Novi Quadrianto, Paula Gordaliza, 2023, 2302.01079.

An investigation of clustering strategies in many-objective optimization: the I-Multi algorithm as a case study

Aurora Trinidad Ramirez Pozo, Roberto Santana, José Antonio Lozano, 2017, Swarm Intelligence.

Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2008, J. Comput. Biol..

Learning Bayesian networks for clustering by means of constructive induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

An Artificial Bioindicator System for Network Intrusion Detection

Christian Blum, José Antonio Lozano, Pedro Pinacho Davidson, 2015, Artificial Life.

Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

Iñaki Inza, José Antonio Lozano, Aritz Pérez Martínez, 2013, Environ. Model. Softw..

A review of estimation of distribution algorithms in bioinformatics

Concha Bielza, Pedro Larrañaga, Yvan Saeys, 2008, BioData Mining.

Distance Measures for Time Series in R: The TSdist Package

Alexander Mendiburu, Usue Mori, Jose A. Lozano, 2016, R J..

Alexander Mendiburu, José Antonio Lozano, Usue Mori, 2016, IEEE Transactions on Knowledge and Data Engineering.

Customized Selection in Estimation of Distribution Algorithms

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2014, SEAL.

Adaptive Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, Adaptive and Multilevel Metaheuristics.

HLA class I and class II frequencies in patients with cutaneous malignant melanoma from southeastern Spain: the role of HLA-C in disease prognosis

J. A. Lozano, M. Álvarez-López, J. A. Campillo, 2005, Immunogenetics.

Performance evaluation of compromise conditional Gaussian networks for data clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, Int. J. Approx. Reason..

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2000, Pattern Recognit. Lett..

Partitional Cluster Analysis with Genetic Algorithms: Searching for the Number of Clusters

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1998 .

Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1997, ICANNGA.

Feature subset selection from positive and unlabelled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2009, Pattern Recognit. Lett..

Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers

Iñaki Inza, José Antonio Lozano, Juan Diego Rodríguez, 2012, Neurocomputing.

Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation

José Antonio Lozano, Aritz Pérez Martínez, Juan Diego Rodríguez, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A study on the complexity of TSP instances under the 2-exchange neighbor system

Alexander Mendiburu, José Antonio Lozano, Leticia Hernando, 2011, 2011 IEEE Symposium on Foundations of Computational Intelligence (FOCI).

Increasing Power of Genome-Wide Association Studies by Collecting Additional Single-Nucleotide Polymorphisms

Eleazar Eskin, Jose A. Lozano, Emrah Kostem, 2011, Genetics.

Machine learning: an indispensable tool in bioinformatics.

Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea, 2010, Methods in molecular biology.

Machine Learning in Bioinformatics

Concha Bielza, Pedro Larrañaga, Iñaki Inza, 2008, Encyclopedia of Database Systems.

Lack of association between HLA-E polymorphism and primary cutaneous melanoma in Spanish patients.

J. A. Lozano, A. Minguela, N. Guerra-Pérez, 2005, Journal of dermatological science.

A partially supervised classification approach to dominant and recessive human disease gene prediction

Pedro Larrañaga, Borja Calvo, Núria López-Bigas, 2007, Comput. Methods Programs Biomed..

Bayesian performance analysis for black-box optimization benchmarking

Ofer M. Shir, Thomas Bäck, Borja Calvo, 2019, GECCO.

Mallows and generalized Mallows model for matchings

Jose A. Lozano, Borja Calvo, Ekhine Irurozki, 2019, Bernoulli.

A Note on the Boltzmann Distribution and the Linear Ordering Problem

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2016, CAEPIA.

Mallows and Generalized Mallows Model for

J. A. Lozano, Ekhine Irurozki, Borja Calvo, 2016 .

The Plackett-Luce ranking model on permutation-based optimization problems

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2013, 2013 IEEE Congress on Evolutionary Computation.

A review on estimation of distribution algorithms in permutation-based combinatorial optimization problems

Alexander Mendiburu, José Antonio Lozano, Ekhine Irurozki, 2012, Progress in Artificial Intelligence.

Weak supervision and other non-standard classification problems: A taxonomy

Iñaki Inza, José Antonio Lozano, Jerónimo Hernández-González, 2016, Pattern Recognit. Lett..

An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization

Jose A. Lozano, Roberto Santana, Alexander Mendiburu, 2019, IEEE Access.

Bayesian optimization for parameter tuning in evolutionary algorithms

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

An Introduction to Evolutionary Algorithms

José Antonio Lozano, J. A. Lozano, 2002, Estimation of Distribution Algorithms.

Experimental Results in Function Optimization with EDAs in Continuous Domain

Pedro Larrañaga, Jose A. Lozano, Endika Bengoetxea, 2002, Estimation of Distribution Algorithms.

A Cheap Feature Selection Approach for the K-Means Algorithm

Marco Capo, Aritz Perez, Jose A Lozano, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Parallel and multi-objective EDAs to create multivariate calibration models for quantitative chemical applications

Alexander Mendiburu, José Antonio Lozano, José Miguel-Alonso, 2005, 2005 International Conference on Parallel Processing Workshops (ICPPW'05).

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Representing the behaviour of supervised classification learning algorithms by Bayesian networks

I Inza, P Larrañaga, B Sierra, 1999, Pattern Recognit. Lett..

On-Line Dynamic Time Warping for Streaming Time Series

Javier Del Ser, José Antonio Lozano, Izaskun Oregi, 2017, ECML/PKDD.

Nature-inspired approaches for distance metric learning in multivariate time series classification

Javier Del Ser, José Antonio Lozano, Izaskun Oregi, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

An evolutionary discretized Lambert approach for optimal long-range rendezvous considering impulse limit

Jose A. Lozano, Josu Ceberio, Abolfazl Shirazi, 2019, Aerospace Science and Technology.

Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study

Iñaki Inza, José Antonio Lozano, Jonathan Ortigosa-Hernández, 2016, IEEE Transactions on Neural Networks and Learning Systems.

Applying genetic algorithms to search for the best hierarchical clustering of a dataset

Pedro Larrañaga, José Antonio Lozano, J. A. Lozano, 1999, Pattern Recognit. Lett..

Multi-objective global optimization of a butterfly valve using genetic algorithms.

José Luis Olazagoitia, J. A. Lozano, Sergio Corbera, 2016, ISA transactions.

Comprehensive characterization of the behaviors of estimation of distribution algorithms

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2015, Theor. Comput. Sci..

Spacecraft trajectory optimization: A review of models, objectives, approaches and solutions

Jose A. Lozano, Josu Ceberio, Abolfazl Shirazi, 2018, Progress in Aerospace Sciences.

Time series classifier recommendation by a meta-learning approach

J. A. Lozano, A. Abanda, U. Mori, 2022, Pattern Recognit..

Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies

José Antonio Lozano, José Miguel-Alonso, Jose Antonio Pascual, 2015, Journal of Grid Computing.

A Distance-Based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem

Alexander Mendiburu, José Antonio Lozano, Ekhine Irurozki, 2014, IEEE Transactions on Evolutionary Computation.

Introducing the Mallows Model on Estimation of Distribution Algorithms

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2011, ICONIP.

A Review on Outlier/Anomaly Detection in Time Series Data pdf

Jose A. Lozano, Usue Mori, Angel Conde, 2020, ACM Comput. Surv..

An Evaluation of Methods for Estimating the Number of Local Optima in Combinatorial Optimization Problems

Alexander Mendiburu, José Antonio Lozano, Leticia Hernando, 2013, Evolutionary Computation.

An efficient K-means clustering algorithm for tall data

José Antonio Lozano, Aritz Pérez Martínez, Marco Capó, 2020, Data Mining and Knowledge Discovery.

Genetic Algorithms: Bridging the Convergence Gap

Pedro Larrañaga, Manuel Graña, José Antonio Lozano, 1999, Theor. Comput. Sci..

On the Taxonomy of Optimization Problems Under Estimation of Distribution Algorithms

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2013, Evolutionary Computation.

A square lattice probability model for optimising the Graph Partitioning Problem

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

EDA++: Estimation of Distribution Algorithms With Feasibility Conserving Mechanisms for Constrained Continuous Optimization

J. A. Lozano, Josu Ceberio, A. Shirazi, 2022, IEEE Transactions on Evolutionary Computation.

In-depth analysis of SVM kernel learning and its components

Jose A. Lozano, Roberto Santana, Ibai Roman, 2020, Neural Computing and Applications.

Estimation of Distribution Algorithms Applied to the Job Shop Scheduling Problem: Some Preliminary Research

Jose A. Lozano, Alexander Mendiburu, J. A. Lozano, 2002, Estimation of Distribution Algorithms.

A review of distances for the Mallows and Generalized Mallows estimation of distribution algorithms

Alexander Mendiburu, José Antonio Lozano, Ekhine Irurozki, 2015, Comput. Optim. Appl..

Using Multidimensional Bayesian Network Classifiers to Assist the Treatment of Multiple Sclerosis

José Antonio Lozano, Aritz Pérez Martínez, Juan Diego Rodríguez, 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

Interactions and dependencies in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2005, 2005 IEEE Congress on Evolutionary Computation.

Fast Fitness Improvements in Estimation of Distribution Algorithms Using Belief Propagation

Jose A. Lozano, Roberto Santana, Alexander Mendiburu, 2012 .

Fish recruitment prediction, using robust supervised classification methods

Jose A. Lozano, Iñaki Inza, Jose A. Fernandes, 2010 .

Probabilistic Load Forecasting Based on Adaptive Online Learning

Santiago Mazuelas, José A. Lozano, J. A. Lozano, 2020, IEEE Transactions on Power Systems.

Research topics in discrete estimation of distribution algorithms based on factorizations

Pedro Larrañaga, Jose A. Lozano, Roberto Santana, 2009, Memetic Comput..

Scatter Search in software testing, comparison and collaboration with Estimation of Distribution Algorithms

José Antonio Lozano, Ramón Sagarna, J. A. Lozano, 2006, Eur. J. Oper. Res..

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems

Pedro Larrañaga, Jose A. Lozano, H. Mühlenbein, 2003 .

Variable search space for software testing

R. Sagarna, J.A. Lozano, J. A. Lozano, 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

Extending distance-based ranking models in estimation of distribution algorithms

Alexander Mendiburu, José Antonio Lozano, Ekhine Irurozki, 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

Different scenarios for survival analysis of evolutionary algorithms

Roberto Santana, José Antonio Lozano, J. A. Lozano, 2017, GECCO.

Distance-Based Exponential Probability Models for Constrained Combinatorial Problems

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2018, CAEPIA.

Computing factorized approximations of Pareto-fronts using mNM-landscapes and Boltzmann distributions pdf

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2015, ArXiv.

Kernels of Mallows Models for Solving Permutation-based Problems

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2015, GECCO.

Evolving MNK-landscapes with structural constraints

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

Mixtures of Generalized Mallows models for solving the quadratic assignment problem

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

A review of message passing algorithms in estimation of distribution algorithms

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2016, Natural Computing.

Critical Issues in Model-Based Surrogate Functions in Estimation of Distribution Algorithms

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2013, SEMCCO.

Message Passing Methods for Estimation of Distribution Algorithms Based on Markov Networks

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2013, SEMCCO.

Structural transfer using EDAs: An application to multi-marker tagging SNP selection

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2012, 2012 IEEE Congress on Evolutionary Computation.

Multi-marker tagging single nucleotide polymorphism selection using estimation of distribution algorithms

Eleazar Eskin, Roberto Santana, Alexander Mendiburu, 2010, Artif. Intell. Medicine.

A Review on Parallel Estimation of Distribution Algorithms

Alexander Mendiburu, José Antonio Lozano, José Miguel-Alonso, 2010, Parallel and Distributed Computational Intelligence.

Analyzing the probability of the optimum in EDAs based on Bayesian networks

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2009, 2009 IEEE Congress on Evolutionary Computation.

Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, PPSN.

Protein Folding in Simplified Models With Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, IEEE Transactions on Evolutionary Computation.

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, J. Heuristics.

Exact Bayesian network learning in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, 2007 IEEE Congress on Evolutionary Computation.

A parallel framework for loopy belief propagation

Endika Bengoetxea, Roberto Santana, Alexander Mendiburu, 2007, GECCO '07.

The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, EvoBIO.

Optimization by Max-Propagation Using Kikuchi Approximations

Pedro Larrañaga, Robin Höns, Jose A. Lozano, 2007 .

Path Planning for Single Unmanned Aerial Vehicle by Separately Evolving Waypoints

José Antonio Lozano, Xianbin Cao, Ke Tang, 2015, IEEE Transactions on Robotics.

Optimization of Deep Learning Precipitation Models Using Categorical Binary Metrics

Jose A. Lozano, Iñaki Inza, Pablo Rozas Larraondo, 2020, Journal of Advances in Modeling Earth Systems.

Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2000, Complex Syst..

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2009, GECCO.

A multiobjective approach to the portfolio optimization problem

José Antonio Lozano, Rubén Armañanzas, J. A. Lozano, 2005, 2005 IEEE Congress on Evolutionary Computation.

Effects of Reducing VMs Management Times on Elastic Applications

José Antonio Lozano, José Miguel-Alonso, Jose Antonio Pascual, 2018, Journal of Grid Computing.

Evolving NK-complexity for evolutionary solvers

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2012, GECCO '12.

Early classification of time series using multi-objective optimization techniques

Alexander Mendiburu, José Antonio Lozano, Usue Mori, 2019, Inf. Sci..

Early Classification of Time Series by Simultaneously Optimizing the Accuracy and Earliness

Sanjoy Dasgupta, Jose A. Lozano, Alexander Mendiburu, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Reliable early classification of time series based on discriminating the classes over time

Eamonn J. Keogh, Alexander Mendiburu, José Antonio Lozano, 2016, Data Mining and Knowledge Discovery.

A Review of Travel Time Estimation and Forecasting for Advanced Traveler Information Systems

Alexander Mendiburu, Usue Mori, Jose A. Lozano, 2012 .

DYNAMIC SEARCH SPACE TRANSFORMATIONS FOR SOFTWARE TEST DATA GENERATION

José Antonio Lozano, Ramón Sagarna, J. A. Lozano, 2008, Comput. Intell..

Assisting in search heuristics selection through multidimensional supervised classification: A case study on software testing

Iñaki Inza, Alexander Mendiburu, José Antonio Lozano, 2014, Inf. Sci..

Learning to classify software defects from crowds: A novel approach

Iñaki Inza, José Antonio Lozano, Daniel Rodríguez, 2018, Appl. Soft Comput..

A Dialogue-Act Taxonomy for a Virtual Coach Designed to Improve the Life of Elderly

M. Inés Torres, María Inés Torres, Jose A. Lozano, 2019, Multimodal Technol. Interact..

An efficient approximation to the K-means clustering for massive data

José Antonio Lozano, Aritz Pérez Martínez, Marco Capó, 2017, Knowl. Based Syst..

A mathematical analysis of EDAs with distance-based exponential models

María Merino, José Antonio Lozano, Imanol Unanue, 2019, Memetic Computing.

A Tunable Generator of Instances of Permutation-Based Combinatorial Optimization Problems

Alexander Mendiburu, José Antonio Lozano, Leticia Hernando, 2016, IEEE Transactions on Evolutionary Computation.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

Iñaki Inza, José Antonio Lozano, Aritz Pérez Martínez, 2015, Ecol. Informatics.

A Method for Wind Speed Forecasting in Airports Based on Nonparametric Regression

Jose A. Lozano, Iñaki Inza, Pablo Rozas-Larraondo, 2014 .

Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2002, Int. J. Approx. Reason..

Combinatonal Optimization by Learning and Simulation of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, Ramon Etxeberria, 2000, UAI.

SNDProb: A Probabilistic Approach for Streaming Novelty Detection

J. A. Lozano, Iñaki Inza, Ander Carreño, 2023, IEEE Transactions on Knowledge and Data Engineering.

Water leak detection using self-supervised time series classification

José Antonio Lozano, Usue Mori, Angel Conde, 2021, Inf. Sci..

Efficient approximation of probability distributions with k-order decomposable models

Iñaki Inza, José Antonio Lozano, Aritz Pérez Martínez, 2016, Int. J. Approx. Reason..

Dealing with the evaluation of supervised classification algorithms

Iñaki Inza, José Antonio Lozano, Guzmán Santafé, 2015, Artificial Intelligence Review.

Multidimensional k-Interaction Classifier: Taking Advantage of All the Information Contained in Low Order Interactions

Iñaki Inza, José Antonio Lozano, Aritz Pérez Martínez, 2013, CAEPIA.

Estimation of the Distribution Algorithm With a Stochastic Local Search for Uncertain Capacitated Arc Routing Problems

Xin Yao, José Antonio Lozano, Ke Tang, 2016, IEEE Transactions on Evolutionary Computation.

Detection of sand dunes on Mars using a regular vine-based classification approach

Roberto Santana, José Antonio Lozano, Diana Carrera, 2019, Knowl. Based Syst..

A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm

Licheng Jiao, José Antonio Lozano, Juanjuan Luo, 2016, IEEE Transactions on Evolutionary Computation.

Side chain placement using estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, Artif. Intell. Medicine.

Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

J. A. Lozano, Roberto Santana, Jon Vadillo, 2020, J. Mach. Learn. Res..

Significance tests or confidence intervals: which are preferable for the comparison of classifiers?

José Antonio Lozano, Daniel P. Berrar, J. A. Lozano, 2013, J. Exp. Theor. Artif. Intell..

A review on distance based time series classification

José Antonio Lozano, Usue Mori, Amaia Abanda, 2018, Data Mining and Knowledge Discovery.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2005, Evolutionary Computation.

Sampling and Learning Mallows and Generalized Mallows Models Under the Cayley Distance

J. A. Lozano, Ekhine Irurozki, Borja Calvo, 2016, Methodology and Computing in Applied Probability.

Evaluation of Parallel EDAs to Create Chemical Calibration Models

Alexander Mendiburu, José Antonio Lozano, José Miguel-Alonso, 2006, 2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06).

Multi-Objectivising Combinatorial Optimisation Problems by Means of Elementary Landscape Decompositions

Jose A. Lozano, Alexander Mendiburu, Borja Calvo, 2019, Evolutionary Computation.

Are we generating instances uniformly at random?

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

A system for airport weather forecasting based on circular regression trees

Iñaki Inza, José Antonio Lozano, Pablo Rozas Larraondo, 2018, Environ. Model. Softw..

Adversarial Sample Crafting for Time Series Classification with Elastic Similarity Measures

Javier Del Ser, José Antonio Lozano, Izaskun Oregi, 2018, IDC.

Mutual information based feature subset selection in multivariate time series classification

José Antonio Lozano, Josu Ircio, Aizea Lojo, 2020, Pattern Recognit..

When and How to Fool Explainable Models (and Humans) with Adversarial Examples

J. A. Lozano, Roberto Santana, Jon Vadillo, 2021, ArXiv.

Construct , Merge , Solve & Adapt : A New General Algorithm For Combinatorial Optimization

Manuel López-Ibáñez, Christian Blum, José Antonio Lozano, 2015 .

Are the Artificially Generated Instances Uniform in Terms of Difficulty?

José Antonio Lozano, Josu Ceberio, Aritz Pérez Martínez, 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

Optimization-based mapping framework for parallel applications

José Antonio Lozano, José Miguel-Alonso, Jose Antonio Pascual, 2011, J. Parallel Distributed Comput..

Strategies to Map Parallel Applications onto Meshes

José Antonio Lozano, José Miguel-Alonso, Jose Antonio Pascual, 2010, DCAI.

A data-driven approach to precipitation parameterizations using convolutional encoder-decoder neural networks

Jose A. Lozano, Pablo Rozas Larraondo, Luigi J. Renzullo, 2019, 1903.10274.

A Boltzmann-Based Estimation of Distribution Algorithm for a General Resource Scheduling Model

Huaping Chen, José Antonio Lozano, Xinle Liang, 2015, IEEE Transactions on Evolutionary Computation.

An Efficient Split-Merge Re-Start for the $K$K-Means Algorithm

Jose A. Lozano, Marco Capó, Aritz Pérez, 2022, IEEE Trans. Knowl. Data Eng..

An efficient evolutionary algorithm for the orienteering problem

María Merino, José Antonio Lozano, Gorka Kobeaga, 2018, Comput. Oper. Res..

Learning Bayesian classifiers from positive and unlabeled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2007, Pattern Recognit. Lett..

Evolving Gaussian Process Kernels for Translation Editing Effort Estimation

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2019, LION.

An efficient K -means clustering algorithm for massive data pdf

José Antonio Lozano, Aritz Pérez Martínez, Marco Capó, 2018, ArXiv.

A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]

José Antonio Lozano, Josu Ircio, Aizea Lojo, 2022, IEEE Computational Intelligence Magazine.

Aggregated outputs by linear models: An application on marine litter beaching prediction

Iñaki Inza, Jerónimo Hernández-González, Oihane C. Basurko, 2019, Inf. Sci..

The linear ordering problem revisited

Alexander Mendiburu, José Antonio Lozano, Josu Ceberio, 2015, Eur. J. Oper. Res..

Parallel implementation of EDAs based on probabilistic graphical models

Alexander Mendiburu, José Antonio Lozano, José Miguel-Alonso, 2005, IEEE Transactions on Evolutionary Computation.

A Summary on the Study of the Medium-Term Forecasting of the Extra-Virgen Olive Oil Price

Antonio J. Rivera, María José del Jesús, M. Dolores Pérez-Godoy, 2011, CAEPIA.

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems

J. A. Lozano, H. Mühlenbein, P. Larrañaga, 2003 .

Symmetry in evolutionary and estimation of distribution algorithms

Roberto Santana, José Antonio Lozano, Robert I. McKay, 2013, 2013 IEEE Congress on Evolutionary Computation.

Multidimensional Learning from Crowds: Usefulness and Application of Expertise Detection

Iñaki Inza, José Antonio Lozano, Jerónimo Hernández-González, 2015, Int. J. Intell. Syst..

Learning from Crowds in Multi-dimensional Classification Domains

Iñaki Inza, José Antonio Lozano, Jerónimo Hernández-González, 2013, CAEPIA.

Multi-view classification of psychiatric conditions based on saccades

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2015, Appl. Soft Comput..

Iterative Probabilistic Tree Search for the Minimum Common String Partition Problem

Christian Blum, José Antonio Lozano, Pedro Pinacho Davidson, 2014, Hybrid Metaheuristics.

Mathematical programming strategies for solving the minimum common string partition problem

Christian Blum, José Antonio Lozano, Pedro Pinacho Davidson, 2014, Eur. J. Oper. Res..

Mixtures of Kikuchi Approximations

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2006, ECML.

Measuring the class-imbalance extent of multi-class problems

Iñaki Inza, José Antonio Lozano, Jonathan Ortigosa-Hernández, 2017, Pattern Recognit. Lett..

Bayesian inference for algorithm ranking analysis

José Antonio Lozano, Borja Calvo, Josu Ceberio, 2018, GECCO.

On solving cycle problems with Branch-and-Cut: extending shrinking and exact subcycle elimination separation algorithms pdf

Jose A. Lozano, Gorka Kobeaga, Mar'ia Merino, 2020, Annals of Operations Research.

Ofﬁcial Journal of the Bernoulli Society for Mathematical Statistics and Probability

J. A. Lozano, Borja Calvo, 2019 .

Learning from Proportions of Positive and Unlabeled Examples

Iñaki Inza, José Antonio Lozano, Jerónimo Hernández-González, 2017, Int. J. Intell. Syst..

Learning Bayesian network classifiers from label proportions

Iñaki Inza, José Antonio Lozano, Jerónimo Hernández-González, 2013, Pattern Recognit..

Robust image classification against adversarial attacks using elastic similarity measures between edge count sequences

Javier Del Ser, José Antonio Lozano, Aritz Pérez Martínez, 2020, Neural Networks.

Evolution of Gaussian Process kernels for machine translation post-editing effort estimation

Jose A. Lozano, Roberto Santana, Alexander Mendiburu, 2021, Annals of Mathematics and Artificial Intelligence.

Conference Report on 2017 IEEE Congress on Evolutionary Computation (IEEE CEC 2017) [Conference Reports]

José Antonio Lozano, J. A. Lozano, 2017, IEEE Comput. Intell. Mag..

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, AISTATS.

An analysis of the use of probabilistic modeling for synaptic connectivity prediction from genomic data

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2012, 2012 IEEE Congress on Evolutionary Computation.

Estimation of Distribution Algorithms

J. A. Lozano, P. Larrañaga, P. Posík, 2002, Genetic Algorithms and Evolutionary Computation.

Fitting the data from embryo implantation prediction: Learning from label proportions

María A Guembe, J. A. Lozano, Iñaki Inza, 2018, Statistical Methods in Medical Research.

Evolving Gaussian Process kernels from elementary mathematical expressions pdf

Jose A. Lozano, Roberto Santana, Alexander Mendiburu, 2019, ArXiv.

Sentiment analysis with genetically evolved gaussian kernels

Roberto Santana, Alexander Mendiburu, José Antonio Lozano, 2019, GECCO.

Vine copula classifiers for the mind reading problem

Roberto Santana, José Antonio Lozano, Diana Carrera, 2016, Progress in Artificial Intelligence.

A review of travel time estimation and forecasting for Advanced Traveller Information Systems

J. A. Lozano, A. Mendiburu, U. Mori, 2015 .

The Convergence Behavior of the PBIL Algorithm: A Preliminary Approach

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2001 .

Merge Nondominated Sorting Algorithm for Many-Objective Optimization

Antonio J. Nebro, J. A. Lozano, J. Moreno, 2018, IEEE Transactions on Cybernetics.

Transfer weight functions for injecting problem information in the multi-objective CMA-ES

Aurora Trinidad Ramirez Pozo, José Antonio Lozano, Olacir Rodrigues Castro Junior, 2016, Memetic Computing.

Trajectory optimization of space vehicle in rendezvous proximity operation with evolutionary feasibility conserving techniques

J. A. Lozano, Josu Ceberio, A. Shirazi, 2023, Eng. Appl. Artif. Intell..

Fuzzy Approach for the Temporary Logistics Hubs’ Selection Planning in Disaster Region

J. A. Lozano, J. Kacprzyk, B. Midodashvili, 2022, 2022 IEEE 11th International Conference on Intelligent Systems (IS).

Gene-Gene Interactions Detection Using a Two-Stage Model

Sagi Snir, Eleazar Eskin, José Antonio Lozano, 2014, RECOMB.

Automating weather forecasts based on convolutional networks

Jose A. Lozano, Iñaki Inza, Pablo Rozas Larraondo, 2017 .

Ad-hoc explanation for time series classification

J. A. Lozano, A. Abanda, U. Mori, 2022, Knowl. Based Syst..

Hill-Climbing Algorithm: Let's Go for a Walk Before Finding the Optimum

Alexander Mendiburu, José Antonio Lozano, Leticia Hernando, 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models

Qingfu Zhang, Pedro Larrañaga, José Antonio Lozano, 2009, IEEE Trans. Evol. Comput..

Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2004, ISBMDA.

Bayesian Model Averaging of Naive Bayes for Clustering

J. A. Lozano, P. Larrañaga, Guzmán Santafé, 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Evolutionary algorithms to optimize low-thrust trajectory design in spacecraft orbital precession mission

José Antonio Lozano, Josu Ceberio, Abolfazl Shirazi, 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).