Antonio Leiva - Academia.edu (original) (raw)

Papers by Antonio Leiva

Proceedings of the 7th International Joint Conference on Computational Intelligence, 2015

Computational devices with significant computing power are pervasive yet often under-exploited si... more Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the form of brief, ephemeral bursts, mainly in the presence of devices "lent" voluntarily by their users. A highly dynamic and volatile computational landscape emerges from the collective contribution of numerous such devices. Algorithms consciously running on these environments require specific properties in terms of flexibility, plasticity and robustness. Bioinspired algorithms are particularly well suited to this endeavor, thanks to their intrinsic features: decentralized functioning, intrinsic parallelism, resilience, and adaptiveness. The latter is essential to exert advanced self-control on the functioning and/or structure of the algorithm. Much has been done in providing self-adaptation capabilities to these techniques, yet the science of self-bionspired algorithms is still nascent, in particular regarding to higher-level self-adaptation, and self-management in the context of large scale optimization problems and distributed ephemeral computing technologies. Deploying bioinspired techniques on this scenario will also pave the way for the application of other techniques on this computational domain.

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, 2007

Finding binary sequences with low autocorrelation is a very hard problem with many practical appl... more Finding binary sequences with low autocorrelation is a very hard problem with many practical applications. In this paper we analyze several metaheuristic approaches to tackle the construction of this kind of sequences. We focus on two different local search strategies, steepest descent local search (SDLS) and tabu search (TS), and their use both as stand-alone techniques and embedded within a memetic algorithm (MA). Plain evolutionary algorithms are shown to perform worse than stand-alone local search strategies. However, a MA endowed with TS turns out to be a stateof-the-art algorithm: it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature.

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2007

Branch-and-Bound and memetic algorithms represent two very different approaches for tackling comb... more Branch-and-Bound and memetic algorithms represent two very different approaches for tackling combinatorial optimization problems. These approaches are not incompatible however. In this paper, we consider a hybrid model that combines these two techniques. To be precise, it is based on the interleaved execution of both approaches. Since the requirements of time and memory in branch-and-bound techniques are generally conflicting, we have opted for carrying out a truncated exact search, namely, beam search. The resulting hybrid algorithm has therefore a heuristic nature. The multidimensional 0-1 knapsack problem and the shortest common supersequence problem have been chosen as benchmarks. As will be shown, the hybrid algorithm can produce better results in both problems at the same computational cost, specially for large problem instances. I. INTRODUCTION Branch-and-bound techniques (BnB) [1] constitute a well-known approach for solving combinatorial optimization problems to optimality. Essentially, BnB techniques use an implicit enumeration scheme for exploring the search space in an intelligent way. This is done by partitioning the search space, producing upper and lower bounds of the solutions attainable in each partition. Thus, the search performed by the algorithm can be represented as a tree traversed in a certain way. The most efficient (in terms of the number of iterations required to find the optimum and prove its optimality) is best-first, i.e., expanding firstly the most promising-according to the local bound-nodes. However, the memory requirements can make this strategy unrealistic for large problem instances. The alternative is to use a depth-first traversal. This strategy does not require large amounts of memory, but it can expand many more nodes than best-first. Besides these simple strategies, leading BnB software also uses more sophisticated selection procedures such as best first with diving (i.e., a mixture of depth-first and best-first) to quickly obtain good incumbent solutions. For an in-depth discussion of search strategies for mixed-integer programming see [2]. On the other hand, evolutionary algorithms (EAs) [3] have a completely different philosophy: tentative solutions are iteratively generated, aiming at producing better and better solutions. Their performance is probably, yet not provably, good: near-optimal solutions can be typically found at an acceptable computational cost in many

This paper presents a procedural content generator method that have been able to generate aesthet... more This paper presents a procedural content generator method that have been able to generate aesthetic maps for a real-time strategy game. The maps has been characterized based on several of their properties in order to define a similarity function between scenarios. This function has guided a multi-objective evolution strategy during the process of generating and evolving scenarios that are similar to other aesthetic maps while being different to a set of non-aesthetic scenarios. The solutions have been checked using a support-vector machine classifier and a self-organizing map obtaining successful results (generated maps have been classified as aesthetic maps).

Theory and Practice of Logic Programming, 2007

In this paper, we present our proposal to Constraint Functional Logic Programming over Finite Dom... more In this paper, we present our proposal to Constraint Functional Logic Programming over Finite Domains (CFLP($\fd$)) with a lazy functional logic programming language which seamlessly embodies finite domain ($\fd$) constraints. This proposal increases the expressiveness and power of constraint logic programming over finite domains (CLP($\fd$)) by combining functional and relational notation, curried expressions, higher-order functions, patterns, partial applications, non-determinism, lazy evaluation, logical variables, types, domain variables, constraint composition, and finite domain constraints. We describe the syntax of the language, its type discipline, and its declarative and operational semantics. We also describe \toy(fd)$, an implementation for CFLP($\fd$), and a comparison of our approach with respect to CLP($\fd$) from a programming point of view, showing the new features we introduce. And, finally, we show a performance analysis which demonstrates that our implementation i...

Lecture Notes in Computer Science, 2013

This paper describes the application of competitive coevolution as a mechanism of self learning i... more This paper describes the application of competitive coevolution as a mechanism of self learning in a two-player real time strategy (RTS) game. The paper presents this (war) RTS game, developed by the authors as an open-source tool, and describes its (built-in) coevolutionary engine developed to find winning strategies. This engine applies a competitive coevolutionary algorithm that uses the concept of Hall-ofFame to establish a long-term memory that is employed in the evaluation process. An empirical analysis of the performance of two different versions of this coevolutionary algorithm is conducted in the context of the RTS game. Moreover, the paper also shows, by an example, the potential of this coevolutionary engine as a prediction tool by inferring the initial conditions (i.e. army configuration) under which a battle has been executed when we know the final result.

Large-Scale Scientific Computing, 2012

One of the lessons learned in the last years in the metaheuristics community, and most prominentl... more One of the lessons learned in the last years in the metaheuristics community, and most prominently in the area of evolutionary computation (EC), is the need of exploiting problem knowledge in order to come up with effective optimization tools. This problem-knowledge can be provided in a variety of ways, but there are situations in which endowing the optimization algorithm with this knowledge is a very elusive task. This may be the case when this problem-awareness is hard to encapsulate within a specific algorithmic description, ...

... I thank him for his time. I want to mention the many wonderful friends I made in Leeds that c... more ... I thank him for his time. I want to mention the many wonderful friends I made in Leeds that contributed to the happy times there. Among them I want to thank especially Fausto Spoto, Karim Kjemame, Javier Nuñez and again Pat. ...

International Journal of Interactive Multimedia and Artificial Intelligence, 2015

Videogames are one of the most important and profitable sectors in the industry of entertainment.... more Videogames are one of the most important and profitable sectors in the industry of entertainment. Nowadays, the creation of a videogame is often a large-scale endeavor and bears many similarities with, e.g., movie production. On the central tasks in the development of a videogame is content generation, namely the definition of maps, terrains, non-player characters (NPCs) and other graphical, musical and AI-related components of the game. Such generation is costly due to its complexity, the great amount of work required and the need of specialized manpower. Hence the relevance of optimizing the process and alleviating costs. In this sense, procedural content generation (PCG) comes in handy as a means of reducing costs by using algorithmic techniques to automatically generate some game contents. PCG also provides advantages in terms of player experience since the contents generated are typically not fixed but can vary in different playing sessions, and can even adapt to the player herself. For this purpose, the underlying algorithmic technique used for PCG must be also flexible and adaptable. This is the case of computational intelligence in general and evolutionary algorithms in particular. In this work we shall provide an overview of the use of evolutionary intelligence for PCG, with special emphasis on its use within the context of realtime strategy games. We shall show how these techniques can address both playability and aesthetics, as well as improving the game AI.

Lecture Notes in Computer Science, 2005

We focus on the Golomb ruler problem, a hard constrained combinatorial optimization problem. Two ... more We focus on the Golomb ruler problem, a hard constrained combinatorial optimization problem. Two alternative encodings are considered, one based on the direct representation of solutions, and one based on the use of an auxiliary decoder. The properties of the corresponding fitness landscapes are analyzed. It turns out that the landscape for the direct encoding is highly irregular, causing drift to low-fitness regions. On the contrary, the landscape for the indirect representation is regular, and exhibits comparable fitness-distance correlation to that of the former landscape. These findings are validated in the context of variable neighborhood search.

Many aspects of Nature, Biology or even from Society have become part of the techniques and algor... more Many aspects of Nature, Biology or even from Society have become part of the techniques and algorithms used in computer science or they have been used to enhance or hybridize several techniques through the inclusion of advanced evolution, cooperation or biologically based additions. The previous NICSO workshops were held in Granada, Spain, 2006, Acireale, Italy, 2007, and in Tenerife, Spain, 2008. As in the previous editions, NICSO 2010, held in Granada, Spain, was conceived as a forum for the latest ideas and the state of the ...

Lecture Notes in Computer Science, 2014

Entertainment Computing, 2014

ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generat... more ABSTRACT This paper presents a procedural content generation (PCG) method that is able to generate aesthetic maps for a real-time strategy game. The maps were characterized based on either their geometrical properties or their topological measures (obtained in this latter case from the sphere-of-influence graph induced by each map). Using these features, a distance function between maps can be defined. This function is used in turn to determine how close/far each map generated by the PCG method (a self-adaptive evolutionary algorithm) is to a collection of maps which were taken initially to be aesthetic or non-aesthetic. This correspondence guided a multi-objective evolutionary approach whereby maps close to aesthetic maps and far to non-aesthetic maps are sought. Self-organizing maps are used to ascertain whether the so-generated maps naturally cluster together with aesthetic maps, as well as to provide a qualitative assessment of the ability of each set of features to characterize the latter.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010

This paper describes a generic (meta-)cooperative optimization schema in which several agents end... more This paper describes a generic (meta-)cooperative optimization schema in which several agents endowed with an optimization technique (whose nature is not initially restricted) cooperate to solve an optimization problem. These agents can use a wide set of optimization techniques, including local search, population-based methods, and hybrids thereof, hence featuring multilevel hybridization. This optimization approach is here deployed on the Tool Switching Problem (ToSP), a hard combinatorial optimization problem in the area of flexible manufacturing. We have conducted an ample experimental analysis involving a comparison of a wide number of algorithms or a large number of instances. This analysis indicates that some meta-cooperative instances perform significantly better than the rest of the algorithms, including a memetic algorithm that was the previous incumbent for this problem.

Studies in Computational Intelligence, 2010

The Tool Switching Problem (ToSP) is a hard combinatorial optimization problem of relevance in th... more The Tool Switching Problem (ToSP) is a hard combinatorial optimization problem of relevance in the field of flexible manufacturing systems (FMS), that has been tackled in the literature using both complete and heuristic methods, including local-search metaheuristics, population-based methods and hybrids thereof (e.g., memetic algorithms). This work approaches the ToSP using several hybrid cooperative models where spatially-structured agents are endowed with specific localsearch/population-based strategies. Issues such as the intervening techniques and the communication topology are analyzed via an extensive empirical evaluation. It is shown that the cooperative models provide better results than their constituent parts. Furthermore, they not only provide solutions of similar quality to those returned by the memetic approach but raise interest prospects with respect to its scalability.