JJ Merelo | University of Granada (original) (raw)

Papers by JJ Merelo

Research paper thumbnail of A Statistical Approach to Dealing with Noisy Fitness in Evolutionary Algorithms

Studies in Computational Intelligence, 2015

Research paper thumbnail of New Evolutionary Selection Operators for Snake Optimizer

Proceedings of the 14th International Joint Conference on Computational Intelligence

Research paper thumbnail of We would also like to acknowledge the valuable input of several additional reviewers

Research paper thumbnail of Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm

Proceedings of the 12th International Joint Conference on Computational Intelligence, 2020

Selecting the proper distance measure is very challenging for most clustering algorithms. Some co... more Selecting the proper distance measure is very challenging for most clustering algorithms. Some common distance measures include Manhattan (City-block), Euclidean, Minkowski, and Chebyshev. The so called Nearest Point with Indexing Ratio (NPIR) is a recent clustering algorithm, which tries to overcome the limitations of other algorithms by identifying arbitrary shapes of clusters, non-spherical distribution of points, and shapes with different densities. It does so by iteratively utilizing the nearest neighbors search technique to find different clusters. The current implementation of the algorithm considers the Euclidean distance measure, which is used for the experiments presented in the original paper of the algorithm. In this paper, the impact of the four common distance measures on NPIR clustering algorithm is investigated. The performance of NPIR algorithm in accordance to purity and entropy measures is investigated on nine data sets. The comparative study demonstrates that the NPIR generates better results when Manhattan distance measure is used compared to the other distance measures for the studied high dimensional data sets in terms of purity and entropy. 2 RELATED WORK Clustering algorithms can be categorized into partitioning algorithms, hierarchical algorithms, and 430

Research paper thumbnail of Automatic classification of biological particles from electron-microscopy images using conventional and genetic-algorithm optimized learning vector quantization

Neural Processing Letters, 1998

Automatic classification of transmission electron-microscopy images is an important step in the c... more Automatic classification of transmission electron-microscopy images is an important step in the complex task of determining the structure of biologial macromolecules. The process of 3D reconstruction from a set of such images implies their previous classification into homogeneous image classes. In general, different classes may represent either distinct biochemical specimens or specimens from different directions of an otherwise homogenous specimen. In this paper, a neural network classification algorithm has been applied to a real-data case in which it was known a priori the existence of two differentiated views of the same specimen. Using two labeled sets as a reference, the parameters and architecture of the classifier were optimized using a genetic algorithm. The global automatic process of training and optimization is implemented using the previously described G-LVQ (genetic learning vector quantization) [10] algorithm, and compared to a non-optimized version of the algorithm, Kohonen's LVQ (learning vector quantization) [7]. Using a part of the sample as training set, the results presented here show an efficient (approximately 90%) average classification rate of unknown samples in two classes. Finally, the implication of this kind of automatic classification of algorithms in the determination of three dimensional structure of biological particles is discused. This paper extends the results already presented in [11], and also improves them.

Research paper thumbnail of A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011

This paper investigates the mutation rates of a Genetic Algorithm (GA) with the sandpile mutation... more This paper investigates the mutation rates of a Genetic Algorithm (GA) with the sandpile mutation. This operator, which was specifically designed for non-stationary (or dynamic) optimization problems, relies on a Self-Organized Criticality system called sandpile to self-adapt the mutation intensity during the run. The behaviour of the operator depends on the state of the sandpile and on the fitness values of the population. Therefore, it has been argued that the mutation distribution may depend on to the severity and frequency of changes and on the type of stationary function that is chosen as a base-function for the dynamic problems. An experimental setup is proposed for investigating these issues. The results show that, at least under the proposed framework, a GA with the sandpile mutation self-adapts the mutation rates to the dynamics of the problem and to the characteristics of the base-function.

Research paper thumbnail of P2P Evolutionary Algorithms: A Suitable Approach for Tackling Large Instances in Hard Optimization Problems

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms

Lecture Notes in Computer Science, 2009

Research paper thumbnail of On the Run-Time Dynamics of a Peer-to-Peer Evolutionary Algorithm

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Application of vector quantization algorithms to protein classification and secondary structure computation

Lecture Notes in Computer Science, 1991

Research paper thumbnail of Optimization of a competitive learning neural network by genetic algorithms

Lecture Notes in Computer Science, 1993

Page 1. OPTIMIZATION OF A COMPETITIVE LEARNING NEURAL NETWORK BY GENETIC ALGORITHMS Merelo, jj.I;... more Page 1. OPTIMIZATION OF A COMPETITIVE LEARNING NEURAL NETWORK BY GENETIC ALGORITHMS Merelo, jj.I; Patdn,M. ~; Caaas, AJ; Prieto,A.~; Mordn,F? 1 Dpto. de Electr6nica y Tecnologfa de Computadores. Facultad de Ciencias. Universidad de Granada. ...

Research paper thumbnail of Clustering and Visualizing HIV Quasispecies Using Kohonen’s Self-Organizing Maps

Lecture Notes in Computer Science, 2007

Research paper thumbnail of Architecture Performance Prediction Using Evolutionary Artificial Neural Networks

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Test pattern generation for analog circuits using neural networks and evolutive algorithms

Lecture Notes in Computer Science, 1995

Research paper thumbnail of Simulation and hardware implementation of competitive learning neural networks

Lecture Notes in Physics, 1990

ABSTRACT One of the main research topics within neuron-like networks is related to learning techn... more ABSTRACT One of the main research topics within neuron-like networks is related to learning techniques. Competitive learning has got an special interest among them, because a great network automation is achieved with it, ie, autonomously and without explicit indication of the correct output patterns, the network extracts general features that can be used in order to cluster a set of patterns. In this paper, after giving a brief overview about learning procedures, the most peculiar characteristics of competitive learning are pointed out, and the different ways of implementing neuron-like networks are quoted, describing as an implementation instance our present project of hardware construction of a neural chip to be included in a coprocessor board with competitive learning.

Research paper thumbnail of SA-prop: Optimization of multilayer perceptron parameters using simulated annealing

Lecture Notes in Computer Science, 1999

Research paper thumbnail of Optimizing web newspaper layout using simulated annealing

Lecture Notes in Computer Science, 1999

Research paper thumbnail of Optimizing web page layout using an annealed genetic algorithm as client-side script

Lecture Notes in Computer Science, 1998

Research paper thumbnail of hCHAC-4, an ACO Algorithm for Solving the Four-Criteria Military Path-finding Problem

Studies in Computational Intelligence, 2008

Research paper thumbnail of Evolving Objects: A General Purpose Evolutionary Computation Library

Lecture Notes in Computer Science, 2002

Research paper thumbnail of A Statistical Approach to Dealing with Noisy Fitness in Evolutionary Algorithms

Studies in Computational Intelligence, 2015

Research paper thumbnail of New Evolutionary Selection Operators for Snake Optimizer

Proceedings of the 14th International Joint Conference on Computational Intelligence

Research paper thumbnail of We would also like to acknowledge the valuable input of several additional reviewers

Research paper thumbnail of Empirical Evaluation of Distance Measures for Nearest Point with Indexing Ratio Clustering Algorithm

Proceedings of the 12th International Joint Conference on Computational Intelligence, 2020

Selecting the proper distance measure is very challenging for most clustering algorithms. Some co... more Selecting the proper distance measure is very challenging for most clustering algorithms. Some common distance measures include Manhattan (City-block), Euclidean, Minkowski, and Chebyshev. The so called Nearest Point with Indexing Ratio (NPIR) is a recent clustering algorithm, which tries to overcome the limitations of other algorithms by identifying arbitrary shapes of clusters, non-spherical distribution of points, and shapes with different densities. It does so by iteratively utilizing the nearest neighbors search technique to find different clusters. The current implementation of the algorithm considers the Euclidean distance measure, which is used for the experiments presented in the original paper of the algorithm. In this paper, the impact of the four common distance measures on NPIR clustering algorithm is investigated. The performance of NPIR algorithm in accordance to purity and entropy measures is investigated on nine data sets. The comparative study demonstrates that the NPIR generates better results when Manhattan distance measure is used compared to the other distance measures for the studied high dimensional data sets in terms of purity and entropy. 2 RELATED WORK Clustering algorithms can be categorized into partitioning algorithms, hierarchical algorithms, and 430

Research paper thumbnail of Automatic classification of biological particles from electron-microscopy images using conventional and genetic-algorithm optimized learning vector quantization

Neural Processing Letters, 1998

Automatic classification of transmission electron-microscopy images is an important step in the c... more Automatic classification of transmission electron-microscopy images is an important step in the complex task of determining the structure of biologial macromolecules. The process of 3D reconstruction from a set of such images implies their previous classification into homogeneous image classes. In general, different classes may represent either distinct biochemical specimens or specimens from different directions of an otherwise homogenous specimen. In this paper, a neural network classification algorithm has been applied to a real-data case in which it was known a priori the existence of two differentiated views of the same specimen. Using two labeled sets as a reference, the parameters and architecture of the classifier were optimized using a genetic algorithm. The global automatic process of training and optimization is implemented using the previously described G-LVQ (genetic learning vector quantization) [10] algorithm, and compared to a non-optimized version of the algorithm, Kohonen's LVQ (learning vector quantization) [7]. Using a part of the sample as training set, the results presented here show an efficient (approximately 90%) average classification rate of unknown samples in two classes. Finally, the implication of this kind of automatic classification of algorithms in the determination of three dimensional structure of biological particles is discused. This paper extends the results already presented in [11], and also improves them.

Research paper thumbnail of A Study on the Mutation Rates of a Genetic Algorithm Interacting with a Sandpile

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2011

This paper investigates the mutation rates of a Genetic Algorithm (GA) with the sandpile mutation... more This paper investigates the mutation rates of a Genetic Algorithm (GA) with the sandpile mutation. This operator, which was specifically designed for non-stationary (or dynamic) optimization problems, relies on a Self-Organized Criticality system called sandpile to self-adapt the mutation intensity during the run. The behaviour of the operator depends on the state of the sandpile and on the fitness values of the population. Therefore, it has been argued that the mutation distribution may depend on to the severity and frequency of changes and on the type of stationary function that is chosen as a base-function for the dynamic problems. An experimental setup is proposed for investigating these issues. The results show that, at least under the proposed framework, a GA with the sandpile mutation self-adapts the mutation rates to the dynamics of the problem and to the characteristics of the base-function.

Research paper thumbnail of P2P Evolutionary Algorithms: A Suitable Approach for Tackling Large Instances in Hard Optimization Problems

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms

Lecture Notes in Computer Science, 2009

Research paper thumbnail of On the Run-Time Dynamics of a Peer-to-Peer Evolutionary Algorithm

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Application of vector quantization algorithms to protein classification and secondary structure computation

Lecture Notes in Computer Science, 1991

Research paper thumbnail of Optimization of a competitive learning neural network by genetic algorithms

Lecture Notes in Computer Science, 1993

Page 1. OPTIMIZATION OF A COMPETITIVE LEARNING NEURAL NETWORK BY GENETIC ALGORITHMS Merelo, jj.I;... more Page 1. OPTIMIZATION OF A COMPETITIVE LEARNING NEURAL NETWORK BY GENETIC ALGORITHMS Merelo, jj.I; Patdn,M. ~; Caaas, AJ; Prieto,A.~; Mordn,F? 1 Dpto. de Electr6nica y Tecnologfa de Computadores. Facultad de Ciencias. Universidad de Granada. ...

Research paper thumbnail of Clustering and Visualizing HIV Quasispecies Using Kohonen’s Self-Organizing Maps

Lecture Notes in Computer Science, 2007

Research paper thumbnail of Architecture Performance Prediction Using Evolutionary Artificial Neural Networks

Lecture Notes in Computer Science, 2008

Research paper thumbnail of Test pattern generation for analog circuits using neural networks and evolutive algorithms

Lecture Notes in Computer Science, 1995

Research paper thumbnail of Simulation and hardware implementation of competitive learning neural networks

Lecture Notes in Physics, 1990

ABSTRACT One of the main research topics within neuron-like networks is related to learning techn... more ABSTRACT One of the main research topics within neuron-like networks is related to learning techniques. Competitive learning has got an special interest among them, because a great network automation is achieved with it, ie, autonomously and without explicit indication of the correct output patterns, the network extracts general features that can be used in order to cluster a set of patterns. In this paper, after giving a brief overview about learning procedures, the most peculiar characteristics of competitive learning are pointed out, and the different ways of implementing neuron-like networks are quoted, describing as an implementation instance our present project of hardware construction of a neural chip to be included in a coprocessor board with competitive learning.

Research paper thumbnail of SA-prop: Optimization of multilayer perceptron parameters using simulated annealing

Lecture Notes in Computer Science, 1999

Research paper thumbnail of Optimizing web newspaper layout using simulated annealing

Lecture Notes in Computer Science, 1999

Research paper thumbnail of Optimizing web page layout using an annealed genetic algorithm as client-side script

Lecture Notes in Computer Science, 1998

Research paper thumbnail of hCHAC-4, an ACO Algorithm for Solving the Four-Criteria Military Path-finding Problem

Studies in Computational Intelligence, 2008

Research paper thumbnail of Evolving Objects: A General Purpose Evolutionary Computation Library

Lecture Notes in Computer Science, 2002

Research paper thumbnail of El ecosistema digital: modelos de comunicación, nuevos medios y público en Internet

Trabajo de carácter coral, surgido a partir de la celebración de una Jornada de difusión científi... more Trabajo de carácter coral, surgido a partir de la celebración de una Jornada de difusión científico - técnica, que tuvo en la Universitat de València en mayo de 2005. El tema de la jornada, "formas de comunicación interpersonal y nuevos paradigmas mediáticos", sirvió para evaluar las principales novedades que en la época supuso la aparición incipiente en Internet de las primeras herramientas producto de la Web 2.0, en particular los weblogs, y dio pie posteriormente a profundizar y sistematizar lo observado en una publicación digital