The Mexican International Conference on Arti?cial Intelligence (MICAI), a yearly international conference organized by the Mexican Society for Arti?cial Intelligence (SMIA), is a major international AI forum and the main event in the academic life of the country's growing AI community. The proceedings of the previous MICAI events were published by Springer in its Lecture Notes in Arti?cial Intelligence (LNAI) series, vol. 1793,2313,2972,3787,4293,4827, and 5317. Since its foundation the conference has been growing in popularity and improving quality. This volume contains the papers presented at the oral sessions of the 8th Mexican International Conference on Arti?cial Intelligence, MICAI 2009, held November 9-13, 2009, in Guanajuato, Mexico. The conference received for ev- uation 215 submissions by 646 authors from 21 countries. This volume contains revised versionsof 63 articles, which after thorough and careful revision were- lected by the international Program Committee. Thus the acceptance rate was 29.3% This book is structured into 18 sections, 17 of which correspond to a c- ference track and are representative of the main current areas of interest for the AI community; the remaining section comprises invited papers. The conference featured excellent keynote lectures by leading AI experts: Patricia Melin, Instituto Tecnologico de Tijuana, Mexico Dieter Hutter, DFKI GmbH, Germany Josef Kittler, Surrey University, UK Ramon Lopez de Mantaras, IIIA-CSIC, Spain Jose Luis Marroquin, CIMAT, Mexico In addition to the oral technical sessions and keynote lectures, the conf- ence program included tutorials, workshops, and a poster session, which were published in separate proceedings volumes."

Multicriterion optimization refers to problems with two or more objectives (n- mally in con?ict with each other) which must be simultaneously satis?ed. Multicriterion optimization problems have not one but a set of solutions (which represent trade-o's among the objectives), which are called Pareto optimal - lutions. Thus, the main goal in multicriterion optimization is to ?nd or to - proximate the set of Pareto optimal solutions. Evolutionary algorithms have been used for solving multicriterion optimization problems for over two decades, gaining an increasing popularity over the last 10 years. The 3rd International Conference on Evolutionary Multi-criterion Optimi- tion(EMO2005)washeldduringMarch9 11,2005, inGuanajuato, M exico.This wasthethirdinternationalconferencededicatedentirelytothisimportanttopic, followingthesuccessfulEMO2001andEMO2003conferences, whichwereheldin Z] urich, SwitzerlandinMarch2001, andinFaro, PortugalinApril2003, respectively. The EMO 2005 scienti?c program included two keynote addresses, one given by Peter Fleming on an engineering design perspective of many-objective op- mization, and the other given by Milan Zeleny on the evolution of optimality. In addition, three tutorials were presented, one on metaheuristics for multiobj- tivecombinatorialoptimizationbyXavierGandibleux, anotheronmultiobjective evolutionary algorithms by Gary B. Lamont, and a third one on performance assessment of multiobjective evolutionary algorithms by Joshua D. Knowle

This volume comprises eight well-versed contributed chapters devoted to report the latest findings on the intelligent approaches to multimedia data analysis. Multimedia data is a combination of different discrete and continuous content forms like text, audio, images, videos, animations and interactional data. At least a single continuous media in the transmitted information generates multimedia information. Due to these different types of varieties, multimedia data present varied degrees of uncertainties and imprecision, which cannot be easy to deal by the conventional computing paradigm. Soft computing technologies are quite efficient to handle the imprecision and uncertainty of the multimedia data and they are flexible enough to process the real-world information. Proper analysis of multimedia data finds wide applications in medical diagnosis, video surveillance, text annotation etc. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent state of the art.