Juan Ramón Velasco Pérez

Anegsys: An automated negotiation based recommender system for local e-marketplaces

Local e-marketplaces are local online e-commerce platforms deployed by product and service providers and accessed by local customers via mobile devices. In this scenario, customers need to gather information about available offers from the different providers in the area, in order to select the most suitable for their needs and preferences. We present ANEGSYS, an agent-based recommender system for product acquisition which uses automatic bilateral negotiations to generate purchase pre-agreements among buyer and seller agents. This greatly enhances the search for solutions which maximize both buyer and seller utilities.

Design of an adaptive Genetic Algorithm for maximizing and minimizing throughput in a computer network

The Genetic Algorithms (GAs) control parameter settings are key factors in the determination of the exploitation versus exploration tradeoff. Adaptive genetic algorithms (AGAs) have been built for inducing exploitation/exploration relationships that improve the final results. One of the most widely studied adaptive approaches are the adaptive parameter setting techniques. Nevertheless, there are no standard rules for choosing appropriate values for these parameters and this decision is usually taken in terms of the most common values or experimental formulas given in literature, or by means of trial an error methods. The paper presents an effective approach based on a meta-level GA combined with an adaptation strategy of the GA control parameters to find and adjust the optimum probabilities to improve the GA performance. In order to validate our approach, an AGA have been designed to drive the generation of a background traffic for maximizing and minimizing throughput in a computer network. Different comparisons are performed, aiming to assess the acceptable optimization power of the proposed system.