Javier Ramírez-Rodríguez

An optimization algorithm inspired by musical composition

In this paper we propose a new multiagent metaheuristic based in an artificial society that uses a dynamic creative system to compose music, called “Method of musical composition” or MMC. To show the performance of our proposed MMC algorithm, 13 benchmark continuous optimization problems and the related results are compared with harmony search, improved harmony search, global-best harmony search and self-adaptative harmony search. The experimental results demonstrate that MMC improves the results obtained by the other metaheuristics in a set of multi-modal functions.

Cognitive-operative model of intelligent learning systems behavior

In this paper behavior during the teaching–learning process is modeled by means of a fuzzy cognitive map. The elements used to model such behavior are part of a generic didactic model, which emphasizes the use of cognitive and operative strategies as part of the student–tutor interaction. Examples of possible initial scenarios for the teaching–learning process are developed, along with the results provided by the model.

Adaptation of the musical composition method for solving constrained optimization problems

Many real-world problems may be expressed as nonlinear constrained optimization problems (CNOP). For this kind of problems, the set of constraints specifies the feasible solution space. In the last decades, several algorithms have been proposed and developed for tackling CNOP. In this paper, we present an extension of the “Musical Composition Method” (MMC) for solving constrained optimization problems. MMC was proposed by Mora et al. (Artif Intell Rev 1–15, doi:10.1007/s10462-011-9309-8, 2012a). The MMC is based on a social creativity system used to compose music. We evaluated and analyzed the performance of MMC on 12 CNOP benchmark cases. The experimental results demonstrate that MMC significantly improves the global performances of the other tested metaheuristics on some benchmark functions.

A model for timetabling problems with period spread constraints

The generalized robust colouring problem (GRCP) deals with a robust colouring for a given graph with a fixed number of colours, not necessarily the chromatic number and considers the distance between colours as the penalization of complementary edges. This problem provides a way to solve timetabling problems that consider ‘event spread constraints’ such as ‘there must be at least d days between two events’. Because this problem is NP-hard, a heuristic approach is necessary to produce good solutions in a reasonable amount of time for large instances. In this work a greedy randomized adaptive search procedure (GRASP) is proposed to solve GRCP, which was used in instances to schedule course lectures requiring from 30 to 120 h per week in total, in which the bound of the optimal solution is reached in almost every instance.

A Graph-Based Summarization System at QA@INEX Track 2011

In this paper we use REG, a graph-based system to study a fundamental problem of Natural Language Processing: the automatic summarization of documents. The algorithm models a document as a graph, to obtain weighted sentences. We applied this approach to the INEX@QA 2011 task (question-answering). We have extracted the title and some key or related words according to two people from the queries, in order to recover 50 documents from english wikipedia. Using this strategy, REG obtained good results with the automatic evaluation system FRESA.

Influence of social network on method musical composition

The method of musical composition (MMC) is a metaheuristic based on sociocultural creativity systems. Within the MMC, models of social influence and social learning are used and integrated in a social network, which is composed of a set of individuals with links between them and involves a set of interaction rules. In this paper, a comparative study on the performance of the MMC with different network structures is proposed. Sixteen benchmark nonlinear optimization problems are solved, taking into account nine social topologies, which are: (a) linear, (b) tree, (c) star, (d) ring, (e) platoons, (f) von Neumann, (g) full connection, (h) random and (i) small world. In addition, the update of each topology structure was tested according to four different strategies: one static, two dynamic and one self-adaptive states. An exhaustive statistical analysis of the obtained numerical results indicates that the social dynamics has no significant impact on the MMC’s behavior. However, the topology structures can be classified into groups that consistently influence the performance level of the MMC. More precisely, a structure characterized by a low value of its mean number of neighbors and a rather fast information transfer process (star topology) performs in a radically opposite way as structures where each agent has many neighbors (random and complete topologies). These observations allow to provide some guidelines for the selection of a network topology used within a social algorithm.

A Knowledge Based System Design for the Prediction of Supernova Effects in the Interstellar Environment

In our work, the design of the elementary components of a knowledge based system for the prediction of supernova effects is described. The domain is related with the modeling of the two supernova effects using fuzzy cognitive maps as knowledge representation and the maps feedback as inference engine to make the prediction. This work also describes the characteristics of (1) the application dominion and its representation, and (2) the results of the model through experimental tests and interpretations which represent the inference process.

Reactive agents to improve a parallel genetic algorithm solution

One of the distributed artificial intelligence objectives is the decentralization of control. Multi-agent architectures distribute the control among two or more agents, which will be in charge of different events. In this paper the design of a parallel Multi-agent architecture for genetic algorithms is described, using a bottom-up behavior design and reactive agents. Such design tries to achieve the improvement solution of parallel genetic algorithms. The purpose of incorporating a reactive behavior in the parallel genetic algorithms is to improve the overall performance by up-dating the sub-populations according to the general behavior of the algorithm avoiding getting stuck in local minima. Two kinds of experiments were conducted for each one of the algorithms and the results obtained with both experiments are shown.