Rodolfo Pazos

Self-Tuning Mechanism for Genetic Algorithms Parameters, an Application to Data-Object Allocation in the Web

In this paper, a new mechanism for automatically obtaining some control parameter values for Genetic Algorithms is presented, which is independent of problem domain and size. This approach differs from the traditional methods which require knowing first the problem domain, and then knowing how to select the parameter values for solving specific problem instances. The proposed method is based on a sample of problem instances, whose solution permits to characterize the problem and to obtain the parameter values.To test the method, a combinatorial optimization model for data-objects allocation in the Web (known as DFAR) was solved using Genetic Algorithms. We show how the proposed mechanism permits to develop a set of mathematical expressions that relates the problem instance size to the control parameters of the algorithm. The experimental results show that the self-tuning of control parameter values of the Genetic Algorithm for a given instance is possible, and that this mechanism yields satisfactory results in quality and execution time. We consider that the proposed method principles can be extended for the self-tuning of control parameters for other heuristic algorithms.

Optimization of the K-means algorithm for the solution of high dimensional instances

This paper addresses the problem of clustering instances with a high number of dimensions. In particular, a new heuristic for reducing the complexity of the K-means algorithm is proposed. Traditionally, there are two approaches that deal with the clustering of instances with high dimensionality. The first executes a preprocessing step to remove those attributes of limited importance. The second, called divide and conquer, creates subsets that are clustered separately and later their results are integrated through post-processing. In contrast, this paper proposes a new solution which consists of the reduction of distance calculations from the objects to the centroids at the classification step. This heuristic is derived from the visual observation of the clustering process of K-means, in which it was found that the objects can only migrate to adjacent clusters without crossing distant clusters. Therefore, this heuristic can significantly reduce the number of distance calculations from an object to the cent...

Design of a shared ontology used for translating negotiation primitives

In this paper we present the design of a shared ontology, with the objective to translate a variety of negotiation primitives. Our approach focuses on facilitating communication among agents during negotiation process execution. Traditional negotiation systems impose several restrictions on the type and format of negotiation primitives that can be exchanged among agents. In contrast, we propose the incorporation of an ontology-based solution to overcome heterogeneity and provide communication facilities for participation in negotiations based in open environments such as Internet. To evaluate our ontology we implemented a Web service-oriented negotiation system, and incorporated a translation module that uses the ontology as a vocabulary of negotiation primitives. The experimental results show that the incorporation of the ontology improves the continuity of the execution of negotiation processes, resulting in more agreements.

Minimizing roundtrip response time in distributed databases with vertical fragmentation

The main purpose of this paper is to show the advantage of using a model proposed by us, which minimizes roundtrip response time versus traditional models that minimize query transmission and processing costs for the design of a distributed database with vertical fragmentation. To this end, an experiment was conducted to compare the roundtrip response time of the optimal solution obtained using our model versus the roundtrip response time of the optimal solution obtained using a traditional model. The experimental results show that for most cases the optimal solution from a traditional model yields a response time which is larger than the response time of the optimal solution obtained from our model, and sometimes it can be thrice as large.

Improvement to the K-Means algorithm through a heuristics based on a bee honeycomb structure

The object clustering problem, according to their similarity measures, can be formulated as a combinatorial optimization problem. The K-Means algorithm has been widely used for solving such problem; however, its computational cost is very high. In this work a new heuristics is proposed for reducing the computational complexity in the classification step of the algorithm based on a honeycomb structure, which the algorithm builds when clusters are visualized in a two-dimensional space. In particular it has been observed that an object can only change membership to neighboring clusters. The heuristics consists of performing distance calculations only with respect to centroids of neighboring clusters, which reduces the number of calculations. For assessing the performance of this heuristics, a set of experiments was carried out that involved 2500, 10000 and 40000 objects uniformly distributed in a two-dimensional space, as well as real-world instances of 3100 and 245 057 objects with 2 and 3 dimensions. The results were encouraging, since the calculation time was reduced 65% on average, with respect to the standard K-Means algorithm for the synthetic instance, and up to 62% on average for the real-world instances, while the quality was reduced on average by 0.05% and 2.5%, respectively.

A causal approach for explaining why a heuristic algorithm outperforms another in solving an instance set of the bin packing problem

The problem of algorithm selection for solving NP problems arises with the appearance of a variety of heuristic algorithms. The first works claimed the supremacy of some algorithm for a given problem. Subsequent works revealed that the supremacy of algorithms only applied to a subset of instances. However, it was not explained why an algorithm solved better an instances subset. In this respect, this work approaches the problem of explaining through causal modeling the interrelations between instances characteristics and the inner workings of algorithms. For validating the results of the proposed approach, a set of experiments was carried out in a study case of the Tabu Search algorithm applied to the Bin Packing problem. Finally, the proposed approach can be useful for redesigning the logic of heuristic algorithms and for justifying the use of an algorithm to solve an instance subset. This information could contribute to algorithm selection for NP-hard problems.

SoapFS: a multiplatform file system

Distributed computer applications usually need processes that allow them to retrieve, store, and share data in a suitable way. As a consequence, file systems become a basic concern for these kinds of applications. Typical file systems have been designed on a computer network infrastructure whose communication and data exchange support is homogeneous. The Internet encourages the construction of file systems, which deal with heterogeneous computer platforms. This work presents SoapFS, a file system that is able to manage information in a heterogeneous environment such as the Internet. This is possible because it is based on technologies like RPC-XML and SOAP. This paper shows how a set of functions available in SoapFS allows the development of robust and heterogeneous distributed applications that involve remote data retrieval and storage. SoapFS shows competitive performance compared with popular file systems and it can connect different file systems working together like a one virtual file system.

An Application of Causality for Representing and Providing Formal Explanations about the Behavior of the Threshold Accepting Algorithm

The problem of algorithm selection for solving NP problems arises with the appearance of a variety of heuristic algorithms. The first works claimed the supremacy of some algorithm for a given problem. Subsequent works revealed the supremacy of algorithms only applied to a subset of instances. However, it was not explained why an algorithm solved better a subset of instances. In this respect, this work approaches the problem of explaining through causal model the interrelations between instances characteristics and the inner workings of algorithms. For validating the results of the proposed approach, a set of experiments was carried out in a study case of the Threshold Accepting algorithm to solve the Bin Packing problem. Finally, the proposed approach can be useful for redesigning the logic of heuristic algorithms and for justifying the use of an algorithm to solve an instance subset. This information could contribute to algorithm selection for NP problems.