Non-Hierarchichal Network Evolutionary System

April 14, 2010

UnrealBots in EVO* 2010 (EVOGames)

Hi,

Here you are the AMAZING (:D) poster presented at EVO* 2010, inside the Workshop EVOGames.

The work describes the analysis, design and implementation of two evolutionary algorithms (a Genetic Algorithm  and a Genetic Programming one) to improve the default Artificial Intelligence of the Bots (authonomous enemies) inside the PC Game Unreal.

It had plenty of visits and millards of interest people ask me about the topics in it…

… well, to be honest just, 10 people ask me (3 of them are friends of mine), but in some moments, around 8 or 10 people were looking at the poster and making jokes with me, since it seems to be a perfect mixture between a fabulous research study (:D) and a funny PC game. 😉

Enjoy it. 😀

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May 19, 2009

Presentation of the multikulti algorithm at CEC09

The Congress of Evolutionary Computation is one of the bigger and more prestigious conferences on, you guessed it, EC. Here are the slides for the talk Lourdes Araújo and myself will be presenting tomorrow.

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.

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