Ms. PacMan in IEEE Transactions on CI and AI in Games

Our fans and followers must be happy! ;D

They can now access the excellent work by Federico Liberatore in IEEE ToCIAIG journal.

This is the best journal concerning Artificial Intelligence in games, with a very strict reviewing process, so, we are very proud of this success. ;)

This is the next step in the research started one year and a half ago designing competitive  Ghost Teams for catching Ms. PacMan.

The abstract is:

In the last year, thanks to the Ms. Pac-Man vs Ghosts competition, the game of Ms. Pac-Man has gained increasing attention from academics in the field of Computational Intelligence. In this work, we contribute to this research stream by presenting a simple Genetic Algorithm with Lexicographic Ranking (GALR) for the optimization of Flocking Strategy-based ghost controllers. Flocking Strategies are a paradigm for intelligent agents characterized by showing emergent behavior and for having very little computational and memory requirements, making them well suited for commercial applications and mobile devices. In particular, we study empirically the effect of optimizing homogeneous and heterogeneous teams. The computational analysis shows that the Flocking Strategy-based controllers generated by the proposed GALR outperform the ghost controllers included in the competition framework and some of those presented in the literature.

The paper can be found here:

Enjoy it!

(And cite us) :D

Volunteer-based evolutionary algorithms al dente

Planning the cook of a time consuming optimization problem? Have you considered to let a crowd of volunteers to help you in this endeavor? In a volunteer-based system,  volunteers provide you with free ingredients (CPU cycles, memory, internet connection,..) to be seasoned with only a pinch of peer-to-peer or desktop-grid technology.

If you are looking for a delicious cook of a volunteer-based evolutionary algorithm, you can find our recipe in this paper published in Genetic Programming and Evolvable Machines (pre-print version available here)

Title: «Designing robust volunteer-based evolutionary algorithms»

Abstract This paper tackles the design of scalable and fault-tolerant evolutionary algorithms computed on volunteer platforms. These platforms aggregate computational resources from contributors all around the world. Given that resources may join the system only for a limited period of time, the challenge of a volunteer-based evolutionary algorithm is to take advantage of a large amount of computational power that in turn is volatile. The paper analyzes first the speed of convergence of massively parallel evolutionary algorithms. Then, it provides some guidance about how to design efficient policies to overcome the algorithmic loss of quality when the system undergoes high rates of transient failures, i.e. computers fail only for a limited period of time and then become available again. In order to provide empirical evidence, experiments were conducted for two well-known problems which require large population sizes to be solved, the first based on a genetic algorithm and the second on genetic programming. Results show that, in general, evolutionary algorithms undergo a graceful degradation under the stress of losing computing nodes. Additionally, new available nodes can also contribute to improving the search process. Despite losing up to 90% of the initial computing resources, volunteer-based evolutionary algorithms can find the same solutions in a failure-prone as in a failure-free run.

Island Model for Multi-Objective Ant Colony Optimization Algorithms in Soft Computing

We are excited because our work «Pareto-based multi-colony multi-objective ant colony optimization algorithms: an island model proposal» has been published in Soft Computing Journal this month, and is now on-line at

Yes, the title is a representation of how long the work is :D

The abstract is:

Multi-objective algorithms are aimed to obtain a set of solutions, called Pareto set, covering the whole Pareto front, i.e. the representation of the optimal set of solutions. To this end, the algorithms should yield a wide amount of near-optimal solutions with a good diversity or spread along this front. This work presents a study on different coarse-grained distribution schemes dealing with Multi-Objective Ant Colony Optimization Algorithms (MOACOs). Two of them are a variation of independent multi-colony structures, respectively having a fixed number of ants in every subset or distributing the whole amount of ants into small sub-colonies. We introduce in this paper a third method: an island-based model where the colonies communicate by migrating ants, following a neighbourhood topology which fits to the search space. All the methods are aimed to cover the whole PF, thus each sub-colony or island tries to search for solutions in a limited area, complemented by the rest of colonies, in order to obtain a more diverse high-quality set of solutions. The models have been tested by implementing them considering three different MOACOs: two well-known and CHAC, an algorithm previously proposed by the authors. Three different instances of the bi-Criteria travelling salesman problem have been considered. The experiments have been performed in a parallel environment (inside a cluster platform), in order to get a time improvement. Moreover, the system scaling factor with respect to the number of processors will be also analysed. The results show that the proposed Pareto-island model and its novel neighbourhood topology performs better than the other models, yielding a more diverse and more optimized set of solutions. Moreover, from the algorithmic point of view, the proposed algorithm, named CHAC, yields the best results on average.

The scheme of the proposed model can be seen in the next figure:

Pareto-based multi-colony island model

Pareto-based multi-colony island model

Enjoy (and CITE) it! :D