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

The Dynamics of Galaxy Pairs in a Cosmological Setting

We use the Millennium Simulation, and an abundance-matching framework, to investigate the dynamical behaviour of galaxy pairs embedded in a cosmological context. Our main galaxy-pair sample, selected to have separations under 250 kpc/h, consists of over 1.3 million pairs at redshift z = 0, with stellar masses greater than 10^9 Msun, probing mass ratios down to 1:1000. We use dark matter halo membership and energy to classify our galaxy pairs. In terms of halo membership, central-satellite pairs tend to be in isolation (in relation to external more massive galaxies), are energetically- bound to each other, and are also weakly-bound to a neighbouring massive galaxy. Satellite-satellite pairs, instead, inhabit regions in close proximity to a more massive galaxy, are energetically-unbound, and are often bound to that neighbour. We find that 60% of our paired galaxies are bound to both their companion and to a third external object. Moreover, only 9% of our pairs resemble the kind of systems described by idealised binary merger simulations in complete isolation. In sum, we demonstrate the importance of properly connecting galaxy pairs to the rest of the Universe.Comment: 25 pages, 14 figures, accepted by MNRA

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

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)

Torque distribution strategy for a four In-wheel fully electric car

Jornadas de Automática, 2 - 4 de septiembre de 2015. BilbaoElectromobility promises to have a strong impact in several aspects of our life: introducing new means of transport concepts, proposing new business models and allowing to create new vehicle configurations impossible with traditional combustion engines. Regarding the latter, this paper presents a novel torque distribution strategy for a 4 in-wheel electric vehicle which aims to reduce the total longitudinal slip. The control strategy is designed off-line supported by a simulator and tested both in simulation (with a different model from the used for designing) as well as on a real sized prototype. The results show that the total longitudinal slip is successfully reduced after applying the control strategy and additionally, the radius described by the vehicle while cornering is slightly closer to the theoretical Ackerman radius.Ministerio de Economía y Competitividad DPI2013-46912-C2-

Mapping galaxy encounters in numerical simulations: The spatial extent of induced star formation

We employ a suite of 75 simulations of galaxies in idealised major mergers (stellar mass ratio ~2.5:1), with a wide range of orbital parameters, to investigate the spatial extent of interaction-induced star formation. Although the total star formation in galaxy encounters is generally elevated relative to isolated galaxies, we find that this elevation is a combination of intense enhancements within the central kpc and moderately suppressed activity at large galacto-centric radii. The radial dependence of the star formation enhancement is stronger in the less massive galaxy than in the primary, and is also more pronounced in mergers of more closely aligned disc spin orientations. Conversely, these trends are almost entirely independent of the encounter's impact parameter and orbital eccentricity. Our predictions of the radial dependence of triggered star formation, and specifically the suppression of star formation beyond kph-scales, will be testable with the next generation of integral-field spectroscopic surveys.Comment: 12 pages, 8 figures, accepted by MNRA

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