His thesis achieved the development of computational methodology for the evaluation of biological knowledge using external information. He has large expertise in the application of Artificial Intelligence to extract and validate knowledge from heterogeneous and huge databases.
Teaching
Computer Science (Information Systems), Pablo de Olavide University.
- Software Engineering II.
- Final Degree Project.
Computer Science, Pablo de Olavide University.
- High Performance Computing.
- Final Master Project.
Related links
Publications
2020 |
D. Rodríguez-Baena; F. Gómez-Vela; M. García-Torres; F. Divina; C. D. Barranco; N. Díaz-Díaz; M. Jiménez; G. Montalvo Identifying livestock behavior patterns based on accelerometer dataset Journal Article In: Journal of Computational Science, vol. 41, pp. 101076, 2020, ISSN: 1877-7503. @article{Rodríguez-Baena2020,In large livestock farming it would be beneficial to be able to automatically detect behaviors in animals. In fact, this would allow to estimate the health status of individuals, providing valuable insight to stock raisers. Traditionally this process has been carried out manually, relying only on the experience of the breeders. Such an approach is effective for a small number of individuals. However, in large breeding farms this may not represent the best approach, since, in this way, not all the animals can be effectively monitored all the time. Moreover, the traditional approach heavily rely on human experience, which cannot be always taken for granted. To this aim, in this paper, we propose a new method for automatically detecting activity and inactivity time periods of animals, as a behavior indicator of livestock. In order to do this, we collected data with sensors located in the body of the animals to be analyzed. In particular, the reliability of the method was tested with data collected on Iberian pigs and calves. Results confirm that the proposed method can help breeders in detecting activity and inactivity periods for large livestock farming. |
2019 |
J. J. Díaz-Montaña; N. Díaz-Díaz; C. D. Barranco; I. Ponzoni Development and use of a Cytoscape app for GRNCOP2 Journal Article In: Computer Methods and Programs in Biomedicine, vol. 177, pp. 211-218, 2019, ISSN: 0169-2607. @article{Díaz-Montaña2019,Background and Objective: Gene regulatory networks (GRNs) are essential for understanding most molecular processes. In this context, the so-called model-free approaches have an advantage modeling the complex topologies behind these dynamic molecular networks, since most GRNs are difficult to map correctly by any other mathematical model. Abstract model-free approaches, also known as rule-based extraction methods, offer valuable benefits when performing data-driven analysis; such as requiring the least amount of data and simplifying the inference of large models at a faster analysis speed. In particular, GRNCOP2 is a combinatorial optimization method with an adaptive criterion for the discretization of gene expression data and high performance, in contrast to other rule-based extraction methods for discovering GRNs. However, the analysis of the large relational structures of the networks inferred by GRNCOP2 requires the support of effective tools for interactive network visualization and topological analysis of the extracted associations. This need motivated the possibility of integrating GRNCOP2 in the Cytoscape ecosystem in order to benefit from Cytoscapes core functionality, as well as all the other apps in its ecosystem. Methods: In this paper, we introduce the implementation of a GRNCOP2 Cytoscape app. This incorporation to Cytoscape platform includes new functionality for GRN visualizations, dynamic user-interaction and integration with other apps for topological analysis of the networks. Results: In order to demonstrate the usefulness of integrating GRNCOP2 in Cytoscape, the new app was used to tackle a novel use case for GRNCOP2: the analysis of crosstalk between pathways. In this regard, datasets associated with Alzheimer’s disease (AD) were analyzed using GRNCOP2 app and other apps of the Cytoscape ecosystem by performing a topological analysis of the AD progression and its synchronization with the Ubiquitin Mediated Proteolysis pathway. Finally, the biological relevance of the findings achieved by this new app were evaluated by searching for evidence in the literature. Conclusions: The proposed crosstalk analysis with the new GRNCOP2 app focused on assessing the phase of the Alzheimer’s disease progression where the coordination with the Ubiquitin Mediated Proteolysis pathway increase, and identifying the genes that explain the signalling between these cellular processes. Both questions were explored by topological contrastive analysis of the GRNs generated for the GRNCOP2 app, where several facilities of Cytoscape were exploited. The topological patterns inferred by this new App have been consistent with biological evidence reported in the scientic literature, illustrating the effectiveness of using this new GRNCOP2 App in pathway analysis. Availability: The GRNCOP2 App is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/grncop2 |
2018 |
A. Lopez-Fernandez; D. Rodríguez-Baena; F. Gómez-Vela; N. Díaz-Díaz BIGO: A web application to analyse gene enrichment analysis results Journal Article In: Computational biology and chemistry, vol. 76, pp. 169-178, 2018, ISSN: 1476-9271. @article{Lopez-Fernandez2018,Background and objective Gene enrichment tools enable the analysis of the relationships between genes with biological annotations stored in biological databases. The results obtained by these tools are usually difficult to analyse. Therefore, researchers require new tools with friendly user interfaces available on all types of devices and new methods to make the analysis of the results easier. Methods In this work, we present the BIGO Web tool. BIGO is a friendly Web tool to perform enrichment analyses of a collection of gene sets. On the basis of the obtained enrichment analysis results, BIGO combines the biological terms to organize them and graphically represents the relationships between gene sets to make the interpretations of the results easier. Results BIGO offers useful services that provide the opportunity to focus on a concrete subset of results by discarding too general biological terms or to obtain useful knowledge by means of the visual analysis of the functional connections between the sets of genes being analysed. Conclusions BIGO is a web tool with a novel and modern design that provides the possibility to improve the analysis tasks applied to gene enrichment results. |
J. J. Díaz-Montaña; F. Gómez-Vela; N. Díaz-Díaz GNC–app: A new Cytoscape app to rate gene networks biological coherence using gene–gene indirect relationships Journal Article In: Biosystems, vol. 166, pp. 61-65, 2018, ISSN: 0303-2647. @article{Díaz-Montaña2018,Motivation Gene networks are currently considered a powerful tool to model biological processes in the Bioinformatics field. A number of approaches to infer gene networks and various software tools to handle them in a visual simplified way have been developed recently. However, there is still a need to assess the inferred networks in order to prove their relevance. Results In this paper, we present the new GNC-app for Cytoscape. GNC-app implements the GNC methodology for assessing the biological coherence of gene association networks and integrates it into Cytoscape. Implemented de novo, GNC-app significantly improves the performance of the original algorithm in order to be able to analyse large gene networks more efficiently. It has also been integrated in Cytoscape to increase the tool accessibility for non-technical users and facilitate the visual analysis of the results. This integration allows the user to analyse not only the global biological coherence of the network, but also the biological coherence at the gene–gene relationship level. It also allows the user to leverage Cytoscape capabilities as well as its rich ecosystem of apps to perform further analyses and visualizations of the network using such data. Availability The GNC-app is freely available at the official Cytoscape app store: http://apps.cytoscape.org/apps/gnc. |
2017 |
J. J. Díaz-Montaña; N. Díaz-Díaz; F. Gómez-Vela GFD-Net: A novel semantic similarity methodology for the analysis of gene networks Journal Article In: Journal of Biomedical Informatics, vol. 68, pp. 71-82, 2017, ISSN: 1532-0464. @article{Díaz-Montaña2017,Since the popularization of biological network inference methods, it has become crucial to create methods to validate the resulting models. Here we present GFD-Net, the first methodology that applies the concept of semantic similarity to gene network analysis. GFD-Net combines the concept of semantic similarity with the use of gene network topology to analyze the functional dissimilarity of gene networks based on Gene Ontology (GO). The main innovation of GFD-Net lies in the way that semantic similarity is used to analyze gene networks taking into account the network topology. GFD-Net selects a functionality for each gene (specified by a GO term), weights each edge according to the dissimilarity between the nodes at its ends and calculates a quantitative measure of the network functional dissimilarity, i.e. a quantitative value of the degree of dissimilarity between the connected genes. The robustness of GFD-Net as a gene network validation tool was demonstrated by performing a ROC analysis on several network repositories. Furthermore, a well-known network was analyzed showing that GFD-Net can also be used to infer knowledge. The relevance of GFD-Net becomes more evident in Section “GFD-Net applied to the study of human diseases†where an example of how GFD-Net can be applied to the study of human diseases is presented. GFD-Net is available as an open-source Cytoscape app which offers a user-friendly interface to configure and execute the algorithm as well as the ability to visualize and interact with the results(http://apps.cytoscape.org/apps/gfdnet). |
2016 |
F. Gómez-Vela; C. D. Barranco; N. Díaz-Díaz Incorporating biological knowledge for construction of fuzzy networks of gene associations Journal Article In: Applied Soft Computing, vol. 42, pp. 144-155, 2016, ISSN: 1568-4946. @article{Gómez-Vela2016,Gene association networks have become one of the most important approaches to modelling of biological processes by means of gene expression data. According to the literature, co-expression-based methods are the main approaches to identification of gene association networks because such methods can identify gene expression patterns in a dataset and can determine relations among genes. These methods usually have two fundamental drawbacks. Firstly, they are dependent on quality of the input dataset for construction of reliable models because of the sensitivity to data noise. Secondly, these methods require that the user select a threshold to determine whether a relation is biologically relevant. Due to these shortcomings, such methods may ignore some relevant information. We present a novel fuzzy approach named FyNE (Fuzzy NEtworks) for modelling of gene association networks. FyNE has two fundamental features. Firstly, it can deal with data noise using a fuzzy-set-based protocol. Secondly, the proposed approach can incorporate prior biological knowledge into the modelling phase, through a fuzzy aggregation function. These features help to gain some insights into doubtful gene relations. The performance of FyNE was tested in four different experiments. Firstly, the improvement offered by FyNE over the results of a co-expression-based method in terms of identification of gene networks was demonstrated on different datasets from different organisms. Secondly, the results produced by FyNE showed its low sensitivity to noise data in a randomness experiment. Additionally, FyNE could infer gene networks with a biological structure in a topological analysis. Finally, the validity of our proposed method was confirmed by comparing its performance with that of some representative methods for identification of gene networks |
J. J. Díaz-Montaña; O. J. L. Rackham; N. Díaz-Díaz; E. Petretto Web-based Gene Pathogenicity Analysis (WGPA): a web platform to interpret gene pathogenicity from personal genome data Journal Article In: Bioinformatics, vol. 32, no. 4, pp. 635-637, 2016, ISBN: 1367-4803. @article{Díaz-Montaña2016,As the volume of patient-specific genome sequences increases the focus of biomedical research is switching from the detection of disease-mutations to their interpretation. To this end a number of techniques have been developed that use mutation data collected within a population to predict whether individual genes are likely to be disease-causing or not. As both sequence data and associated analysis tools proliferate, it becomes increasingly difficult for the community to make sense of these data and their implications. Moreover, no single analysis tool is likely to capture all relevant genomic features that contribute to the gene’s pathogenicity. Here, we introduce Web-based Gene Pathogenicity Analysis (WGPA), a web-based tool to analyze genes impacted by mutations and rank them through the integration of existing prioritization tools, which assess different aspects of gene pathogenicity using population-level sequence data. Additionally, to explore the polygenic contribution of mutations to disease, WGPA implements gene set enrichment analysis to prioritize disease-causing genes and gene interaction networks, therefore providing a comprehensive annotation of personal genomes data in disease. |
2015 |
F. Gómez-Vela; J. A. Lagares; N. Díaz-Díaz Gene network coherence based on prior knowledge using direct and indirect relationships Journal Article In: Computational Biology and Chemistry, vol. 56, pp. 142-151, 2015, ISSN: 1476-9271. @article{Gómez-Vela2015,Gene networks (GNs) have become one of the most important approaches for modeling biological processes. They are very useful to understand the different complex biological processes that may occur in living organisms. Currently, one of the biggest challenge in any study related with GN is to assure the quality of these GNs. In this sense, recent works use artificial data sets or a direct comparison with prior biological knowledge. However, these approaches are not entirely accurate as they only take into account direct gene–gene interactions for validation, leaving aside the weak (indirect) relationships. We propose a new measure, named gene network coherence (GNC), to rate the coherence of an input network according to different biological databases. In this sense, the measure considers not only the direct gene–gene relationships but also the indirect ones to perform a complete and fairer evaluation of the input network. Hence, our approach is able to use the whole information stored in the networks. A GNC JAVA-based implementation is available at: http://fgomezvela.github.io/GNC/. The results achieved in this work show that GNC outperforms the classical approaches for assessing GNs by means of three different experiments using different biological databases and input networks. According to the results, we can conclude that the proposed measure, which considers the inherent information stored in the direct and indirect gene–gene relationships, offers a new robust solution to the problem of GNs biological validation. |
2013 |
N. Díaz-Díaz Genes functional coherence based on actual biological knowledge Journal Article In: AI Communications, vol. 26, no. 2, pp. 247-249, 2013. @article{Díaz-Díaz2013,This work proposes two new approaches to establish the quality of genetic model based on current biological knowledge. First, it is developed a KEGG-based tool that provides a friendly graphical environment to analyze gene-enrichment. Moreover, a novel GO-based dissimilarity measure is proposed for evaluating groups of genes based on the most relevant functions of the whole set. To found this function, an heuristic approach based on Voronoi diagram has been presented. |
2011 |
N. Díaz-Díaz; F. Gómez-Vela; J. Aguilar-Ruiz; J. García-Gutiérrez Gene-gene interaction based clustering method for microarray data Conference 2011 11th International Conference on Intelligent Systems Design and Applications, 2011, ISSN: 2164-7151. @conference{Díaz-Díaz2011b,In this paper, we propose a greedy clustering algorithm to identify groups of related genes and a new measure to improve the results of this algorithm. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. In order to avoid noise in clusters, we apply a threshold, the neighbouring minimun index(?), to know if a pair of genes have interaction enough or not. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene-gene mapping function and filtering function, and even the neighbouring minimun index, and provides much flexibility to obtain clusters based on the level of precision needed. We have carried out a deep experimental study in databases to obtain a good neighbouring minimun index, ?. The performance of our approach is experimentally tested on the yeast, yeast cell-cycle and malaria datasets. The final number of clusters has a very high level of customization and genes within show a significant level of cohesion, as it is shown graphically in the experiments. |
F. Gómez-Vela; F. Martínez-Álvarez; C. D. Barranco; N. Díaz-Díaz; D. Rodríguez-Baena; J. Aguilar-Ruiz Pattern Recognition in Biological Time Series Journal Article In: Advances in Artificial Intelligence, pp. 164-172, 2011, ISBN: 978-3-642-25274-7. @article{Gómez-Vela2011b,Knowledge extraction from gene expression data has been one of the main challenges in the bioinformatics field during the last few years. In this context, a particular kind of data, data retrieved in a temporal basis (also known as time series), provide information about the way a gene can be expressed during time. This work presents an exhaustive analysis of last proposals in this area, particularly focusing on those proposals using non--supervised machine learning techniques (i.e. clustering, biclustering and regulatory networks) to find relevant patterns in gene expression. |
F. Gómez-Vela; N. Díaz-Díaz; J. Aguilar-Ruiz Gene Networks Validation based on Metabolic Pathways Conference 2011 IEEE 11th International Conference on Bioinformatics and Bioengineering, 2011. @conference{Gómez-Vela2011,In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation. Nowadays, a lot of gene network algorithms have been developed as knowledge extraction techniques. A very important task in all these studies is to assure the network models reliability in order to prove that the methods used are precise. This validation process can be carried out by using the inherent information of the input data or by using public biological knowledge. In this last case, these sources of information provide a great opportunity of verifying the biological soundness of the generated networks. In this work, authors present a gene network validation methodology based on the information stored in Kegg database. With this aim, a complete Kegg pathway conversion to gene network is presented, and a global and functional validation process is proposed, where the whole metabolical information stored in Kegg is used at the same time. |
J. Aguilar-Ruiz; D. Rodríguez-Baena; N. Díaz-Díaz; I. A. Nepomuceno-Chamorro CarGene: Characterisation of sets of genes based on metabolic pathways analysis Journal Article In: International Journal of Data Mining and Bioinformatics, vol. 5, no. 5, pp. 558-573, 2011. @article{Aguilar-Ruiz2011,The great amount of biological information provides scientists with an incomparable framework for testing the results of new algorithms. Several tools have been developed for analysing gene-enrichment and most of them are Gene Ontology-based tools. We developed a Kyoto Encyclopedia of Genes and Genomes (Kegg)-based tool that provides a friendly graphical environment for analysing gene-enrichment. The tool integrates two statistical corrections and simultaneously analysing the information about many groups of genes in both visual and textual manner. We tested the usefulness of our approach on a previous analysis (Huttenshower et al.). Furthermore, our tool is freely available (http://www.upo.es/eps/bigs/cargene.html). |
N. Díaz-Díaz; J. Aguilar-Ruiz GO-based Functional Dissimilarity of Gene Sets Journal Article In: BMC Bioinformatics, vol. 12, no. 360, 2011. @article{Díaz-Díaz2011c,Background The Gene Ontology (GO) provides a controlled vocabulary for describing the functions of genes and can be used to evaluate the functional coherence of gene sets. Many functional coherence measures consider each pair of gene functions in a set and produce an output based on all pairwise distances. A single gene can encode multiple proteins that may differ in function. For each functionality, other proteins that exhibit the same activity may also participate. Therefore, an identification of the most common function for all of the genes involved in a biological process is important in evaluating the functional similarity of groups of genes and a quantification of functional coherence can helps to clarify the role of a group of genes working together. Results To implement this approach to functional assessment, we present GFD (GO-based Functional Dissimilarity), a novel dissimilarity measure for evaluating groups of genes based on the most relevant functions of the whole set. The measure assigns a numerical value to the gene set for each of the three GO sub-ontologies. Conclusions Results show that GFD performs robustly when applied to gene set of known functionality (extracted from KEGG). It performs particularly well on randomly generated gene sets. An ROC analysis reveals that the performance of GFD in evaluating the functional dissimilarity of gene sets is very satisfactory. A comparative analysis against other functional measures, such as GS2 and those presented by Resnik and Wang, also demonstrates the robustness of GFD. |
N. Díaz-Díaz; F. Gómez-Vela; D. Rodríguez-Baena; J. Aguilar-Ruiz Gene Regulatory Networks Validation Framework Based in KEGG Conference Hybrid Artificial Intelligent Systems, 2011, ISBN: 978-3-642-21222-2. @conference{Díaz-Díaz2011,In the last few years, DNA microarray technology has attained a very important role in biological and biomedical research. It enables analyzing the relations among thousands of genes simultaneously, generating huge amounts of data. The gene regulatory networks represent, in a graph data structure, genes or gene products and the functional relationships between them. These models have been fully used in Bioinformatics because they provide an easy way to understand gene expression regulation. |
2007 |
I. A. Nepomuceno-Chamorro; J. Aguilar-Ruiz; N. Díaz-Díaz; D. Rodríguez-Baena; J. García A Deterministic Model to Infer Gene Networks from Microarray Data Conference Intelligent Data Engineering and Automated Learning - IDEAL 2007, 2007, ISBN: 978-3-540-77226-2. @conference{Nepomuceno-Chamorro2007,Microarray experiments help researches to construct the structure of gene regulatory networks, i.e., networks representing relationships among different genes. Filter and knowledge extraction processes are necessary in order to handle the huge amount of data produced by microarray technologies. We propose regression trees techniques as a method to identify gene networks. Regression trees are a very useful technique to estimate the numerical values for the target outputs. They are very often more precise than linear regression models because they can adjust different linear regressions to separate areas of the search space. In our approach, we generate a single regression tree for each genes from a set of genes, taking as input the remaining genes, to finally build a graph from all the relationships among output and input genes. In this paper, we will simplify the approach by setting an only seed, the gene ARN1, and building the graph around it. The final model might gives some clues to understand the dynamics, the regulation or the topology of the gene network from one (or several) seeds, since it gathers relevant genes with accurate connections. The performance of our approach is experimentally tested on the yeast Saccharomyces cerevisiae dataset (Rosetta compendium). |
D. Rodríguez-Baena; N. Díaz-Díaz; J. Aguilar-Ruiz; I. A. Nepomuceno-Chamorro Discovering alpha–Patterns from Gene Expression Data Conference Intelligent Data Engineering and Automated Learning - IDEAL 2007, 2007, ISBN: 978-3-540-77226-2. @conference{Rodríguez-Baena2007,The biclustering techniques have the purpose of finding subsets of genes that show similar activity patterns under a subset of conditions. In this paper we characterize a specific type of pattern, that we have called ?–pattern, and present an approach that consists in a new biclustering algorithm specifically designed to find ?–patterns, in which the gene expression values evolve across the experimental conditions showing a similar behavior inside a band that ranges from 0 up to a pre–defined threshold called ?. The ? value guarantees the co–expression among genes. We have tested our method on the Yeast dataset and compared the results to the biclustering algorithms of Cheng & Church (2000) and Aguilar & Divina (2005). Results show that the algorithm finds interesting biclusters, grouping genes with similar behaviors and maintaining a very low mean squared residue. |
2006 |
J. Aguilar-Ruiz; J. A. Nepomuceno; N. Díaz-Díaz; I. A. Nepomuceno-Chamorro A Measure for Data Set Editing by Ordered Projections Conference Advances in Applied Artificial Intelligence, 2006, ISBN: 978-3-540-35454-3. @conference{Aguilar-Ruiz2006,In this paper we study a measure, named weakness of an example, which allows us to establish the importance of an example to find representative patterns for the data set editing problem. Our approach consists in reducing the database size without losing information, using algorithm patterns by ordered projections. The idea is to relax the reduction factor with a new parameter, ?, removing all examples of the database whose weakness verify a condition over this ?. We study how to establish this new parameter. Our experiments have been carried out using all databases from UCI-Repository and they show that is possible a size reduction in complex databases without notoriously increase of the error rate. |
N. Díaz-Díaz; D. Rodríguez-Baena; I. A. Nepomuceno-Chamorro; J. Aguilar-Ruiz Neighborhood-Based Clustering of Gene-Gene Interactions Conference Intelligent Data Engineering and Automated Learning -- IDEAL 2006, 2006, ISBN: 978-3-540-45487-8. @conference{Díaz-Díaz2006,In this work, we propose a new greedy clustering algorithm to identify groups of related genes. Clustering algorithms analyze genes in order to group those with similar behavior. Instead, our approach groups pairs of genes that present similar positive and/or negative interactions. Our approach presents some interesting properties. For instance, the user can specify how the range of each gene is going to be segmented (labels). Some of these will mean expressed or inhibited (depending on the gradation). From all the label combinations a function transforms each pair of labels into another one, that identifies the type of interaction. From these pairs of genes and their interactions we build clusters in a greedy, iterative fashion, as two pairs of genes will be similar if they have the same amount of relevant interactions. Initial two–genes clusters grow iteratively based on their neighborhood until the set of clusters does not change. The algorithm allows the researcher to modify all the criteria: discretization mapping function, gene–gene mapping function and filtering function, and provides much flexibility to obtain clusters based on the level of precision needed. The performance of our approach is experimentally tested on the yeast dataset. The final number of clusters is low and genes within show a significant level of cohesion, as it is shown graphically in the experiments. |
2005 |
R. Ruiz; J. Aguilar-Ruiz; J. C. Riquelme; N. Díaz-Díaz Analysis of Feature Rankings for Classification Conference Advances in Intelligent Data Analysis VI, 2005, ISBN: 978-3-540-31926-9. @conference{Ruiz2005,Different ways of contrast generated rankings by feature selection algorithms are presented in this paper, showing several possible interpretations, depending on the given approach to each study. We begin from the premise of no existence of only one ideal subset for all cases. The purpose of these kinds of algorithms is to reduce the data set to each first attributes without losing prediction against the original data set. In this paper we propose a method, feature–ranking performance, to compare different feature–ranking methods, based on the Area Under Feature Ranking Classification Performance Curve (AURC). Conclusions and trends taken from this paper propose support for the performance of learning tasks, where some ranking algorithms studied here operate. |
R. Giráldez; N. Díaz-Díaz; I. A. Nepomuceno-Chamorro; J. Aguilar-Ruiz An Approach to Reduce the Cost of Evaluation in Evolutionary Learning Conference Computational Intelligence and Bioinspired Systems, 2005, ISBN: 978-3-540-32106-4. @conference{Giráldez2005,The supervised learning methods applying evolutionary algorithms to generate knowledge model are extremely costly in time and space. Fundamentally, this high computational cost is fundamentally due to the evaluation process that needs to go through the whole datasets to assess their goodness of the genetic individuals. Often, this process carries out some redundant operations which can be avoided. In this paper, we present an example reduction method to reduce the computational cost of the evolutionary learning algorithms by means of extraction, storage and processing only the useful information in the evaluation process. |