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.

Towards automatic StarCraft strategy generation using genetic programming

I forgot to mention that we published our paper «Towards automatic StarCraft strategy generation using genetic programming» in CIG 2015 conference, held in Taiwan. This was a work made in collaboration with Alberto Tonda (INRA) and Giovanni Squillero (Politecnico di Torino), starting a new research line using this game (and also, starting other nice collaborations that are still a secret!)

The abstract:

Among Real-Time Strategy games few titles have enjoyed the continued success of StarCraft. Many research lines aimed at developing Artificial Intelligences, or “bots”, capable of challenging human players, use StarCraft as a platform. Several characteristics make this game particularly appealing for researchers, such as: asymmetric balanced factions, considerable complexity of the technology trees, large number of units with unique features, and potential for optimization both at the strategical and tactical level. In literature, various works exploit evolutionary computation to optimize particular aspects of the game, from squad formation to map exploration; but so far, no evolutionary approach has been applied to the development of a complete strategy from scratch. In this paper, we present the preliminary results of StarCraftGP, a framework able to evolve a complete strategy for StarCraft, from the building plan, to the composition of squads, up to the set of rules that define the bot’s behavior during the game. The proposed approach generates strategies as C++ classes, that are then compiled and executed inside the OpprimoBot open-source framework. In a first set of runs, we demonstrate that StarCraftGP ultimately generates a competitive strategy for a Zerg bot, able to defeat several human-designed bots.

Do you want to know more? Download the paper draft or electronic version in IEEE web.

There can be only one: Evolving RTS Bots via joust selection

During EvoStar, our group presented several papers on games, multiobjective optimization and implementation of evolutoinary algorithms. This paper was presented as a full talk at EvoGAMES 2016 in Porto (Portugal). BY: Antonio Fernández Ares, Pablo García-Sánchez, Antonio M. Mora García, Pedro A. Castillo, Juan J. Merelo

Source: There can be only one: Evolving RTS Bots via joust selection