Improved Genetic Fuzzy Drivers presented at CIG 2018

Last week I presented at IEEE CIG 2018 (held in Maastricht, The Netherlands) our following step in our research about autonomous drivers for Car Racing Simulators, such as TORCS, titled “The Evolutionary Race: Improving the Process of Evaluating Car Controllers in Racing Simulators“.

As commented before by @jjmerelo and later by @fergunet, we designed with Mohammed Salem (University of Mascara) a driver’s AI in which two Fuzzy Subcontrollers were hybridized with a Genetic Algorithm.

In this work we present a better evaluation approach for the GA, combining three methods: heuristic track choosing, improved fitness functions, and race-based selection of the best.

The abstract of the work is:

Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms, including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.

And the presentation is:

You can check our paper in the proceedings of the conference.

Enjoy it!

(And cite us as usual :D)


Starcraft GP nominated to the HUMIES award

This year we participated in the HUMIES awards with our paper “Towards Automatic StarCraft Strategy Generation Using Genetic Programming“, accepted at CIG2015, wrote in collaboration with Politecnico di Torino and INRA. Our paper was elected from 28 candidates to be part of the 8 finalists, so we can consider it a great achievement. Although we didn’t won, because the astounding quality of the other works, we are thrilled about our nomination :)

Here is the presentation. It even includes a reference to Starship Troopers!

How good are different languages at runnig evolutionary algorithms?

As part of the EvoStar conference, which took place last week, we presented the poster Benchmarking Languages for Evolutionary Algorithms, where, with help from many friends in Open Science fashion, we tested several a bunch of compiled and scripting languages on several common evolutionary operations: crossover, mutation and OneMax.

It was presented in poster form, and you had to be there to actually understand it. Since you are not, it’s better if you use this comments (or those at the poster) to inquire about it. Or you can check out the interactive presentation we also did, which in fact includes data and everything in the source.
This work is ongoing, and you are very welcome to participate. Just take a peek at the repo, and do a pull request.

Aplicando algoritmos genéticos a un controlador ‘fuzzy’ para una gestión adaptativa del tráfico

Recientemente se ha aceptado el artículo “A hybrid Fuzzy Genetic Algorithm for an adaptive traffic signal system” en la revista open-access Advances in Fuzzy Systems. En él participamos varios de los investigadores de GeNeura.

El abstract es:

This paper presents a hybrid algorithm that combines Fuzzy Logic Controller (FLC) and Genetic Algorithms (GAs) and its application on a traffic signal system. FLCs have been widely used in many applications in diverse areas, such as control system, pattern recognition, signal processing, and forecasting. They are, essentially, rule-based systems, in which the definition of these rules and fuzzy membership functions is generally based on verbally formulated rules that overlap through the parameter space. They have a great influence over the performance of the system. On the other hand, the Genetic Algorithm is a metaheuristic that provides a robust search in complex spaces. In this work, it has been used to adapt the decision rules of FLCs that define an intelligent traffic signal system, obtaining a higher performance than a classical FLC-based control. The simulation results yielded by the hybrid algorithm show an improvement of up to 34% in the performance with respect to a standard traffic signal controller, Conventional Traffic Signal Controller (CTC), and up to 31% in the comparison with a traditional logic controller, FLC.

Esperamos que os guste (y que lo citéis, claro :D).

Enviada la versión definitiva del trabajo “Predicción a muy corto plazo de series temporales de volumen de tráfico rodado mediante co-evolución de RNFBR”

Tras realizar las correspondientes modificaciones indicadas por los revisores del congreso MAEB 2015, hemos procedido a enviar la versión definitiva del trabajo, usando para ello la famosa plataforma EasyChair.
Cabe recordar que MAEB’2015 se celebrará en Mérida-Almendralejo, durante los días 4 al 6 de Febrero de 2015, y estará organizada por el Centro Universitario de Mérida, el cual pertenece a la Universidad de Extremadura.
Toda la info del congreso en:

Tenemos el segundo bot más humano DEL MUNDO…

…jugando a Unreal Tournament 2004. :D

José L. Jiménez, estudiante de informática de la Universidad de Málaga, dirigido por Antonio Fernández Leiva y por mí ha creado un bot, llamado NizorBot, a partir del ExpertBot que hicieron Francisco Aisa y Ricardo Caballero, que ha obtenido el segundo puesto (considerando la humanidad (humanness) del mismo) en el BotPrize 2014, celebrado dentro del CIG 2014.

Human or Bot competition (BotPrize)

Resultados de la competición BotPrize 2014 (primeros puestos)

La competición se basa en un Test de Turing que los bots deben pasar frente a jueces humanos. Éstos se enfrentarán a los bots en combates (Deathmatch) dentro del juego Unreal Tournament 2004. Durante dichos combates (en varias tandas), los jueces marcarán a cada uno de sus rivales como humano o bot en base a su criterio sobre el comportamiento que observen en el jugador.

Además, este año se ha añadido la presencia de un gran número de jueces off-line, es decir, ‘fuera del juego’, que visualizan los enfrentamientos y deciden también qué jugadores son humanos y cuáles no.

Esto le da más valor al resultado que otros años, ya que estos jueces cuentan con un punto de vista ‘menos limitado’ que los participantes en el juego.

El bot ha sido implementado mediante algoritmos evolutivos interactivos, en los que un controlador humano interviene cada cierto tiempo en dos sentidos:

  • Ajustando parámetros del algoritmo (controlador experto en el algoritmo)
  • Respondiendo a cuestiones sobre el jugador y el juego (controlador experto en el juego).

En ambos casos, el desarrollo del algoritmo (su ejecución) se ve afectado y dirigido por este controlador, lo que hace que el bot evolucione de una forma más acorde con lo que éste espera. En este caso, para comportarse de la manera más humana posible.

José está en proceso de escritura de su Proyecto Fin de Carrera y, a continuación, los tres trabajaremos en un artículo describiendo todo el proceso.

¡Estad atentos! :D

[Paper] A Methodology to Develop Service Oriented Evolutionary Algorithms

Paper about our methodology for service oriented EAs

OSGiLiath Evolutionary Framework

Our paper A Methodology to Develop Service Oriented Evolutionary Algorithms has been published in the proceedings of the 8th International Symposium on Intelligent Distributed Computing – IDC’2014. This paper is a resume of the SOA-EA methodology, one of the chapters of my thesis (available here). The presentation, made by JJ Merelo, is quite cool:

The abstract:

This paper proposes a methodology to design and implement Evolutionary Algorithms using the Service Oriented Architecture paradigm. This paradigm allows to deal with some of the shortcomings in the Evolutionary Algorithms area, facilitating the development, integration, standardization of services that conform a evolutionary algorithm, and, besides, the dynamic alteration of those elements in runtime.
A four-step methodology to design services for Evolutionary Algorithms is presented: identification, specification, implementation and deployment. Also, as an example of application of this methodology, an adaptive algorithm is developed.

You can download the paper draft from…

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