Past week I presented my research line to other young researchers of the CITIC-UGR, inside the CITICoffee meetings (a Science Coffee to discuss about our work, without bosses or pressure, but with coffee and pastries!).
Although the slides are in Spanish, there are also diagrams with text in English, so it is not difficult to follow. They also include a Jackie Chan meme!
This paper is a part of my Final Degree Project and it’s the result of our participation in the Google AI Contest of 2010. It’s also my first presentation in an conference, and the first time in English. In this paper we talk about the design of a bot that can play (and win) to the game Planet Wars. In this post we can read the rules of the contest and the game.
In this paper, we study the impact of the noisy fitness in the desing of the bot, because the choose of a bad fitness can make useless the genetic algorithm.
In this work, we present the results obtained from comparing several migration policies that tries to optimize in a noisy fitness environment: the on-line, on-board and hybrid evolutionary robotics problem. Three different migration policies have been studied (the most different migrant, random migrant and best migrant) and two replacement mechanisms: the migrant replaces the worst, or the migrant replaces the worst after being evaluated only if is better. Experiments with 4, 16 and 36 robots were conduced, with two different topologies (ring and panmictic) and also a comparison with other evolutionary robotics algorithms were performed. Results show that the replacement mechanism has more influence than the migration policy or topology, and it also affects the tuning of the algorithm parameters. We asked ourselves the next questions:
Using the hybrid approach (island model), which is the best combination of migration policy, admission policy, and island topology?
Is this combination better than the encapsulated and distributed alternatives?
Does the number of robots affect the result and if so, how?
Conclusions, graphs and stuff and in the paper, but summarizing, multikulti technique (receive the most different individual of my population from other islands) and accept it in my population after its evaluation perform better than other alternatives, even with less migration rate.
We investigate on-line on-board evolution of robot controllers based on the so-called hybrid approach (island-based). Inherently to this approach each robot hosts a population (island) of evolving controllers and exchanges controllers with other robots at certain times. We compare different exchange (migration) policies in order to optimize this evolutionary system and compare the best hybrid setup with the encapsulated and distributed alternatives. We conclude that adding a difference-based migrant selection scheme increases the performance.
Tomorrow we will be presenting the work “Validating a Peer-to-Peer Evolutionary Algorithm” in Evo* 2012 held in Malaga, Spain. You can find below the abstract and presentation of the work.
This paper proposes a simple experiment for validating a Peer-to-Peer Evolutionary Algorithm in a real computing infrastructure in order to verify that results meet those obtained by simulations. The validation method consists of conducting a well-characterized experiment in a large computer cluster of up to a number of processors equal to the population estimated by the simulator. We argue that the validation stage is usually missing in the design of large-scale distributed meta-heuristics given the difficulty of harnessing a large number of computing resources. That way, most of the approaches in the literature focus on studying the model viability throughout a simulation-driven experimentation. However, simulations assume idealistic conditions that can influence the algorithmic performance and bias results when conducted in a real platform. Therefore, we aim at validating simulations by running a real version of the algorithm. Results show that the algorithmic performance is rather accurate to the predicted one whilst times-to-solutions can be drastically decreased when compared to the estimation of a sequential run.