On November 5 and 6 the Instituto Tecnologico de Informatica, Universidad Politecnica de Valencia, Spain organises JICAN 2009, the Workshop on Computational Intelligence Applied to Business.
This is a unique 2-day workshop exclusively devoted to real-world applications of CI, with the support of the Spanish Chapter of IEEE-Computational Intelligence Society.
The purpose of the workshop is twofold: on the one hand, to introduce this technology to companies in any area; on the other, to show actual fields of application to researchers in academia.
We are privileged to count on the participation of world leading experts in Computational Intelligence, who will present the latest developments in CI with a special focus on how it is used in real world business and industry applications (specifically in videogames, logistics, social networks, planning and scheduling…)..
The workshop is aimed at:
Companies: Project managers or directors (technical and non technical) who, as part of their work, face complex problems related to optimisation, prediction and decision making.
Academia: Researchers and students interested in the practical application of Computational Intelligence.
The languages of the Workshop are English and Spanish. Two-way simultaneous translation will be provided to those who require it.
Our paper “Multiobjective Genetic Programming Approach for a Smooth Modeling of the Release Kinetics of a Pheromone Dispenser” was presented at the workshop on Symbolic Regression and Modeling, part of the Genetic and Evolutionary Computation Conference, GECCO, held in Montreal, Canada, from July 8th ot 12th, 2009.
Here’s the abstract:
The accurate modeling of the release kinetics of pheromone dispensers is a matter or great importance for ensuring that the dispenser field-life covers the right period of the pest and for optimizing the layout of dispensers in the treated area.
A new experimental dispenser has been recently designed by researchers at the Instituto Agroforestal del Mediterraneo – Centro de Ecologia Quimica Agricola (CEQA) of the Universidad Politecnica de Valencia (Spain). The most challenging problem for the modeling of the release kinetics of this dispensers is the dificulty in obtaining experimental measurements for building the model. The procedure for obtaining these data is very costly, both time and money wise, therefore the available data across the whole season are scarce. In prior work we demonstrated the utility of using Genetic Programming (GP) for this particular problem. However, the models evolved by the GP algorithm tend to have discontinuities in those time ranges where there are not available measurements. In this work we propose the use of a multiobjective Genetic Programming for modeling the performance of the CEQA dispenser. We take two approaches, involving two and nine objectives respectively. In the first one, one of the objectives of the GP algorithm deals with how well the model fits the experimental data, while the second objective measures how “smooth” the model behaviour is. In the second approach we have as many objectives as data points and the aim is to predict each point separately using the remaining ones. The results obtained endorse the utility of this approach for those modeling problems characterized by the lack of experimental data.
El Jueves 21 de Mayo de 2009 se celebró el seminario “La IA en la adquisición y uso del conocimiento médico: Patrones Asistenciales” en la ETSI Informática de la UNED. El seminario presentaba el proyecto HYGIA (TIN2006-15453), el cual integra el trabajo conjunto desarrollado por las universidades de Santiago de Compostela, Jaume I y Rovira i Virgili, y del Hospital Clínico de Barcelona en el ámbito de la utilización de técnicas de análisis automático de textos, ingeniería del conocimiento y aprendizaje automático inductivo para la generación de patrones asistenciales. Estos patrones formalizan el conocimiento terapéutico de una o varias enfermedades y son capaces de ser interpretados por sistemas informáticos de soporte a la toma de decisiones en medicina. En esta ponencia se presentó el trabajo y los resultados obtenidos en el análisis automático de textos médicos y en el aprendizaje inductivo de patrones a partir de las bases de datos hospitalarias.
La web oficial del proyecto es: http://banzai-deim.urv.net/~riano/TIN2006-15453/
The paper is available under request, but I guess it will be available shortly from IEEExplore. Here’s the abstract
Migration policies in distributed evolutionary algorithms are bound to have, as much as any other evolutionary operator, an impact on the overall performance. However, they have not been an active area of research until recently, and this research has concentrated on the migration rate. In this paper we compare different migration policies, including our proposed ‘multikulti’ methods, which choose the individuals that are going to be sent to other nodes based on the principle of ‘multiculturalism’: the individual sent should be as different as possible to the receiving population (represented in several possible ways). We have checked this policy on two discrete optimization problems for different number of nodes, and found that, in average or in median, multikulti policies outperform others like sending the best or a random individual; however, their advantage changes with the number of nodes involved and the difficulty of the problem. The success of this kind of policies will be explained via the measurement of entropies, which are known to have an impact in the performance of the evolutionary algorithm.
NoHNES members Anna Esparcia and Ken Sharman attended EvoStar 2009, the premier European event on Evolutionary Computation. Anna served as co-chair of EvoNUM (the 2nd European Workshop on Bio-inspired algorithms for continuous parameter optimisation) and EvoGAMES (the 1st European Workshop on Bio-inspired Algorithms in Games).
Our work on modeling pheromone dispensers for sexual confusion in agricluture was presented by Anna Esparcia in the 2nd European workshop on Bio-inspired algorithms for continuous parameter optimisation, EvoNUM 2009, which is part of EvoStar 2009, the premier European event on Evolutionary Computation.
Although the presentation was scheduled at the ungodly hour of 10:30, the comments were higly favourable. As in the previous occasion where we have presented this work, the application was received with great interest, partly because computer scientists are in general unaware of where their food comes from.
However, unlike in MAEB 2009, this time we did not get what has become a memorable comment: “Us people from Madrid only go to the country to check that cows are in fact not purple”.
There are three main EC conferences in the yearly calendar: GECCO, Genetic and Evolutionary Computation Conference, which takes place usually around July in far away places like Canada and London and is organized by the ACM, CEC, Congress on Evolutionary Computation, which also travels around the world but never gets close to Spain, takes place by the end of May/beginning of June, and then Evostar, which is mainly an European event, held in cozy places to an enthusiastic audience.
I’ve never been in CEC, just once in GECCO , and several times in Evostar. From a strictly personal point of view, Evostar is probably the best, but scientifically GECCO is tops, with 5-6 reviewers per paper, a very strong scientif committee, and usually quite interesting keynotes. Papers accepted in GECCO’s main tracks do undergo a reviewing process stronger than many conferences.
All this introduction is meant to say that our project will be this year in all the conferences in the cycle. We’ll be presenting a paper on CEQA at Evostar, our multikulti algorithm at CEC, and another paper on multikulti and a joint work with Christian Gagné at GECCO, as we recently knew yesterday.
So, if you’re attending any of these EC conferences this year, there will be somebody from this project. See you there!
In it we use Genetic Programming to generate models of prediction of book loses in companies, and we tackle the problem of finding the relevant variables for the prediction using Genetic Programming and Kohonen’s Self Organising Maps. This approach singificantly reduces the number of variables used for the analysis, while minimising the prediction error.
In other words, we tell you what you have to look at if you want to avoid losing money in your company – but don’t hold us responsible if you don’t like the results!
The chapter can also be obtained from SpringerLink.
You should also check our financial prediction tool PRESAGI
