A multi-objective genetic graph-based clustering algorithm with memory optimization

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. H. D. Menéndez, D. F. Barrero, and D. Camacho, "A multi-objective genetic graph-based clustering algorithm with memory optimization", in 2013 IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 3174 - 3181Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic Algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.This work has been partly supported by: Spanish Ministry of Science and Education under project TIN2010-19872

Confidence intervals of success rates in evolutionary computation

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 12th annual conference on Genetic and evolutionary computation , http://dx.doi.org/10.1145/1830483.1830657Success Rate (SR) is a statistic straightforward to use and interpret, however a number of non-trivial statistical issues arises when it is examinated in detail. We address some of those issues, providing evidence that suggests that SR follows a binomial density function, therefore its statistical properties are independent of the flavour of the Evolutionary Algorithm (EA) and its domain. It is fully described by the SR and the number of runs. Moreover, the binomial distribution is a well known statistical distribution with a large corpus of tools available that can be used in the context of EC research. One of those tools, confidence intervals (CIs), is studie

Adapting Searchy to extract data using evolved wrappers

This is the author’s version of a work that was accepted for publication inExpert Systems with Applications: An International Journal. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications: An International Journal, 39, 3 (2012) DOI: 10.1016/j.eswa.2011.08.168Organizations need diverse information systems to deal with the increasing requirements in information storage and processing, yielding the creation of information islands and therefore an intrinsic difficulty to obtain a global view. Being able to provide such an unified view of the -likely heterogeneous-information available in an organization is a goal that provides added-value to the information systems and has been subject of intense research. In this paper we present an extension of a solution named Searchy, an agent-based mediator system specialized in data extraction and Integration. Through the use of a set of wrappers, it integrates information from arbitrary sources and semantically translates them according to a mediated scheme. Searchy is actually a domain-independent wrapper container that ease wrapper development, providing, for example, semantic mapping. The extension of Searchy proposed in this paper introduces an evolutionary wrapper that is able to evolve wrappers using regular expressions. To achieve this, a Genetic Algorithm (GA) is used to learn a regex able to extract a set of positive samples while rejects a set of negative samples.The authors gratefully acknowledge Mart´ın Knoblauch for his useful suggestions and valuable comments. This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects ABANT (TIN 2010-19872), COMPUBIODIVE (TIN2007-65989) and by Castilla-La Mancha project PEII09-0266-6640

An empirical study on the accuracy of computational effort in Genetic Programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castaño, and D. Camacho, "An empirical study on the accuracy of computational effort in Genetic Programming", in IEEE Congress on Evolutionary Computation (CEC), 2011, pp. 1164 - 1171Some commonly used performance measures in Genetic Programming are those defined by John Koza in his first book. These measures, mainly computational effort and number of individuals to be processed, estimate the performance of the algorithm as well as the difficulty of a problem. Although Koza's performance measures have been widely used in the literature, their behaviour is not well known. In this paper we study the accuracy of these measures and advance in the understanding of the factors that influence them. In order to achieve this goal, we report an empirical study that attempts to systematically measure the effects of two variability sources in the estimation of the number of individuals to be processed and the computational effort. The results obtained in those experiments suggests that these measures, in common experimental setups, and under certain circumstances, might have a high relative error.This work was partially supported by the MICYT project ABANT (TIN2010-19872) and Castilla-La Mancha project PEII09- 0266-664

Improved targeting through collaborative decision-making and brain computer interfaces

This paper reports a first step toward a brain-computer interface (BCI) for collaborative targeting. Specifically, we explore, from a broad perspective, how the collaboration of a group of people can increase the performance on a simple target identification task. To this end, we requested a group of people to identify the location and color of a sequence of targets appearing on the screen, and measured the time and the accuracy of the response. The individual results are compared to a collective identification result determined by simple majority voting, with random choice in case of drawn. The results are promising, as the identification becomes significantly more reliable even with this simple voting, and with a small number of people (either odd or even) involved in the decision. In addition, the paper briefly analyzes the role of brain-computer interfaces in collaborative targeting, extending the targeting task by using a BCI instead of a mechanical response

A decision support system for logistics operations

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-13161-5_14Proceedings of 5th International Workshop Soft Computing Models in Industrial and Environmental ApplicationsThis paper describes an Artificial Intelligence based application for a logistic company that solves the problem of grouping by zones the packages that have to be delivered and propose the routes that the drivers should follow. The tool combines from the one hand, Case-Based Reasoning techniques to separate and learn the most frequent areas or zones that the experienced logistic operators do. These techniques allow the company to separate the daily incidents that generate noise in the routes, from the decision made based on the knowledge of the route. From the other hand, we have used Evolutionary Computation to plan optimal routes from the learning areas and evaluate those routes. The application allows the users to decide under what parameters (i.e. distance, time, etc) the route should be optimized.We want to thank Antonio Montoya for his contribution in the tool developed. This work has been supported by the Espi & Le Barbier company and the public projects funded by the Spanish Ministry of Science and Innovation under the projects COMPUBIODIVE (TIN2007-65989), V-LeaF (TIN2008-02729-E/TIN) and by Castilla-La Mancha project PEII09- 0266-6640

Effects of the lack of selective pressure on the expected run-time distribution in genetic programming

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. D. F. Barrero, M. D. R-Moreno, B. Castano, and D. Camacho, "Effects of the lack of selective pressure on the expected run-time distribution in genetic programming", in IEEE Congress on Evolutionary Computation, CEC 2013, pp. 1748 - 1755Run-time analysis is a powerful tool to analyze algorithms. It is focused on studying the time required by an algorithm to find a solution, the expected run-time, which is one of the most relevant algorithm attributes. Previous research has associated the expected run-time in GP with the lognormal distribution. In this paper we provide additional evidence in that regard and show how the algorithm parametrization may change the resulting run-time distribution. In particular, we explore the influence of the selective pressure on the run-time distribution in tree-based GP, finding that, at least in two problem instances, the lack of selective pressure generates an expected run-time distribution well described by the Weibull probability distribution.This work has been partly supported by Spanish Ministry of Science and Education under project ABANT (TIN2010- 19872)

Distributed parameter tuning for genetic algorithms

Genetic Algorithms (GA) is a family of search algorithms based on the mechanics of natural selection and biological evolution. They are able to efficiently exploit historical information in the evolution process to look for optimal solutions or approximate them for a given problem, achieving excellent performance in optimization problems that involve a large set of dependent variables. Despite the excellent results of GAs, their use may generate new problems. One of them is how to provide a good fitting in the usually large number of parameters that must be tuned to allow a good performance. This paper describes a new platform that is able to extract the Regular Expression that matches a set of examples, using a supervised learning and agent-based framework. In order to do that, GA-based agents decompose the GA execution in a distributed sequence of operations performed by them. The platform has been applied to Language induction problem, for that reason the experiments are focused on the extraction of the regular expression that matches a set of examples. Finally, the paper shows the efficiency of the proposed platform (in terms of fitness value) applied to three case studies: emails, phone numbers and URLs. Moreover, it is described how the codification of the alphabet affects to the performance of the platform.This work has been partially supported by the Spanish Ministry of Science and Innovation under the projects COMPUBIODIVE(TIN2007-65989), V-LeaF (TIN2008-02729-E/TIN), Castilla-La Mancha project PEII09-0266-6640 and HADA (TIN2007-64718)

Acquisition of business intelligence from human experience in route planning

This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 2015, available online at:http://www.tandfonline.com/10.1080/17517575.2012.759279The logistic sector raises a number of highly challenging problems. Probably one of the most important ones is the shipping planning, i.e., plan the routes that the shippers have to follow to deliver the goods. In this paper we present an AI-based solution that has been designed to help a logistic company to improve its routes planning process. In order to achieve this goal, the solution uses the knowledge acquired by the company drivers to propose optimized routes. Hence, the proposed solution gathers the experience of the drivers, processes it and optimizes the delivery process. The solution uses Data Mining to extract knowledge from the company information systems and prepares it for analysis with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical business intelligence knowledge from the drivers experience that is needed by the planner. The design of the routes is done by a Genetic Algorithm (GA) that, given the processed information, optimizes the routes following several objectives, such as minimize the distance or time. Experimentation shows that the proposed approach is able to find routes that improve, in average, the routes made by the human experts.This work has been partially supported by the SpanishMinistry of Science and Innovation under the projects ABANT (TIN 2010-19872) and by Jobssy.com company under Project FUAM-076913

Clustering avatars behaviours from Virtual Worlds interactions

This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 4th International Workshop on Web Intelligence & Communities, http://dx.doi.org/10.1145/2189736.2189743Virtual Worlds (VWs) platforms and applications provide a practical implementation of the Metaverse concept. These applications, as highly inmersive and interactive 3D environments, have become very popular in social networks and games domains. The existence of a set of open platforms like OpenSim or OpenCobalt have played a major role in the popularization of this technology and they open new exciting research areas. One of these areas is behaviour analysis. In virtual world, the user (or avatar) can move and interact within an artificial world with a high degree of freedom. The movements and iterations of the avatar can be monitorized, and hence this information can be analysed to obtain interesting behavioural patterns. Usually, only the information related to the avatars conversations (textual chat logs) are directly available for processing. However, these open platforms allow to capture other kind of information like the exact position of an avatar in the VW, what they are looking at (eye-gazing) or which actions they perform inside these worlds. This paper studies how this information, can be extracted, processed and later used by clustering methods to detect behaviour or group formations in the world. To detect the behavioural patterns of the avatars considered, clustering techniques have been used. These techniques, using the correct data preprocessing and modelling, can be used to automatically detect hidden patterns from data.This work has been partly supported by: Spanish Ministry of Science and Education under the project TIN2010-1987