Antonio M. Mora

A network analysis of the 2010 FIFA world cup champion team play

This paper analyzes the network of passes among the players of the Spanish team during the last FIFA World Cup 2010, where they emerged as the champion, with the objective of explaining the results obtained from the behavior at the complex network level. The team is considered a network with players as nodes and passes as (directed) edges. A temporal analysis of the resulting passes network is also done, looking at the number of passes, length of the chain of passes, and to network measures such as player centrality and clustering coefficient. Results of the last three matches (the decisive ones) indicate that the clustering coefficient of the pass network remains high, indicating the elaborate style of the Spanish team. The effectiveness of the opposing team in negating the Spanish game is reflected in the change of several network measures over time, most importantly in drops of the clustering coefficient and passing length/speed, as well as in their being able in removing the most talented players from the central positions of the network. Spain’s ability to restore their combinative game and move the focus of the game to offensive positions and talented players is shown to tilt the balance in favor of the Spanish team.

Resilience to churn of a peer-to-peer evolutionary algorithm

In this paper we analyse the resilience of a peer-to-peer (P2P) evolutionary algorithm (EA) subject to the following dynamics: computing nodes acting as peers leave the system independently from each other causing a collective effect known as churn. Since the P2P EA has been designed to tackle large instances of computationally expensive problems, we will assess its behaviour under these conditions, by performing a scalability analysis in five different scenarios using the massively multimodal deceptive problem as a benchmark. In all cases, the P2P EA reaches the success criterion without a penalty on the runtime. We show that the key to the algorithm resilience is to ensure enough peers at the beginning of the experiment; even if some of them leave, those that remain contain enough information to guarantee a reliable convergence.

Effect of Noisy Fitness in Real-Time Strategy Games Player Behaviour Optimisation Using Evolutionary Algorithms

This paper investigates the performance and the results of an evolutionary algorithm (EA) specifically designed for evolving the decision engine of a program (which, in this context, is called bot) that plays Planet Wars. This game, which was chosen for the Google Artificial Intelligence Challenge in 2010, requires the bot to deal with multiple target planets, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is initially based on a set of rules that have been defined after an empirical study, and a genetic algorithm (GA) is used for tuning the set of constants, weights and probabilities that those rules include, and therefore, the general behaviour of the bot. Then, the bot is supplied with the evolved decision engine and the results obtained when competing with other bots (a bot offered by Google as a sparring partner, and a scripted bot with a pre-established behaviour) are thoroughly analysed. The evaluation of the candidate solutions is based on the result of non-deterministic battles (and environmental interactions) against other bots, whose outcome depends on random draws as well as on the opponents’ actions. Therefore, the proposed GA is dealing with a noisy fitness function. After analysing the effects of the noisy fitness, we conclude that tackling randomness via repeated combats and reevaluations reduces this effect and makes the GA a highly valuable approach for solving this problem.

Implementation matters: programming best practices for evolutionary algorithms

While a lot of attention is usually devoted to the study of different components of evolutionary algorithms or the creation of heuristic operators, little effort is being directed at how these algorithms are actually implemented. However, the efficient implementation of any application is essential to obtain a good performance, to the point that performance improvements obtained by changes in implementation are usually much bigger than those obtained by algorithmic changes, and they also scale much better. In this paper we will present and apply usual methodologies for performance improvement to evolutionary algorithms, and show which implementation options yield the best results for a certain problem configuration and which ones scale better when features such as population or chromosome size increase.

Optimizing player behavior in a real-time strategy game using evolutionary algorithms

This paper describes an Evolutionary Algorithm for evolving the decision engine of a bot designed to play the Planet Wars game. This game, which has been chosen for the Google Artificial Intelligence Challenge in 2010, requires that the artificial player is able to deal with multiple objectives, while achieving a certain degree of adaptability in order to defeat different opponents in different scenarios. The decision engine of the bot is based on a set of rules that have been defined after an empirical study. Then, an Evolutionary Algorithm is used for tuning the set of constants, weights and probabilities that define the rules, and, therefore, the global behavior of the bot. The paper describes the Evolutionary Algorithm and the results attained by the decision engine when competing with other bots. The proposed bot defeated a baseline bot in most of the playing environments and obtained a ranking position in top-20% of the Google Artificial Intelligence competition.

Evolving bot AI in Unreal

This paper describes the design, implementation and results of an evolutionary bot inside the PC game UnrealTM, that is, an autonomous enemy which tries to beat the human player and/or some other bots. The default artificial intelligence (AI) of this bot has been improved using two different evolutionary methods: genetic algorithms (GAs) and genetic programming (GP). The first one has been applied for tuning the parameters of the hard-coded values inside the bot AI code. The second method has been used to change the default set of rules (or states) that defines its behaviour. Both techniques yield very good results, evolving bots which are capable to beat the default ones. The best results are yielded for the GA approach, since it just does a refinement following the default behaviour rules, while the GP method has to redefine the whole set of rules, so it is harder to get good results.

Cloud-based evolutionary algorithms: An algorithmic study

This paper presents a cloud-computing based evolutionary algorithm using a synchronous storage service as pool for exchange information among population of solutions. The multi-computer was composed of several normal PCs or laptops connected via Wifi or Ethernet. In this work the effect of how the distributed evolutionary algorithm reached the solution when new PCs was added was tested whether that effect also translates to the algorithmic performance of the algorithm. To this end different (and hard) problems was addressed using the proposed multi-computer, analyzing the effects that the automatic load-balancing and synchronization had on the speed of algorithm successful, and analyzing how the number of evaluation per second increases when the multi-computer includes new nodes. The measure used for the analysis was number of evaluation per second which was increased when the multi-computer includes new nodes. The algorithm solved the proposed problems and it was viable to run it in homogeneous or heterogeneous platforms. The experiments includes two problems and different configuration for the distributed evolutionary algorithm in order to check the results of the algorithm for several rates of information exchange with the selected storage service. Results shows that the system is viable with homogeneous or heterogeneous nodes and there is no significative differences for the synchronous storage services we have tested. But when the problem is harder, and the threads of the algorithm does not stop for each information exchange (migration of individual from one population to another one), the differences of using a specific service became significative in terms of success of the algorithm.

Testing the Intermediate Disturbance Hypothesis: Effect of Asynchronous Population Incorporation on Multi-Deme Evolutionary Algorithms

In P2P and volunteer computing environments, resources are not always available from the beginning to the end, getting incorporated into the experiment at any moment. Determining the best way of using these resources so that the exploration/exploitation balance is kept and used to its best effect is an important issue. The Intermediate Disturbance Hypothesis states that a moderate population disturbance (in any sense that could affect the population fitness) results in the maximum ecological diversity. In the line of this hypothesis, we will test the effect of incorporation of a second population in a two-population experiment. Experiments performed on two combinatorial optimization problems, MMDP and P-Peaks , show that the highest algorithmic effect is produced if it is done in the middle of the evolution of the first population; starting them at the same time or towards the end yields no improvement or an increase in the number of evaluations needed to reach a solution. This effect is explained in the paper, and ascribed to the intermediate disturbanceproduced by first-population immigrants in the second population.

P2P Evolutionary Algorithms: A Suitable Approach for Tackling Large Instances in Hard Optimization Problems

In this paper we present a distributed Evolutionary Algorithm (EA) whose population is structured using newscast, a gossiping protocol. This algorithm has been designed to deal with computationally expensive problems via massive scalability; therefore, we analyse the response time of the model using large instances of well-known hard optimization problems that require from EAs a (sometimes exponentially) bigger computational effort as these problems scale. Our approach has been matched against a sequential Genetic Algorithm (sGA) applied to the same set of problems, and we found that it needs less computational effort than the sGA in yielding success. Furthermore, the response time scales logarithmically with respect to the problem size, which makes it suitable to tackle large instances.

Comparing multiobjective evolutionary ensembles for minimizing type I and II errors for bankruptcy prediction

In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimize both error types in classification: artificial neural networks and function trees ensembles created through multiobjective optimization. Since the multiobjective optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.