Multiobjective evolutionary algorithms (MOEA) are an effective tool for solving search and optimization problems containing several incommensurable and possibly conflicting objec...
There is an increasing demand for locality-preserving distribution of complex data structures in peer-to-peer systems. Current systems either do not preserve object locality or su...
In the last few years, the growing complexity of the electrical power networks, mainly due to the increased use of electronic converters together with the requirements of a higher ...
Andrea Benigni, Gabriele D'Antona, U. Ghisla, Anto...
In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
Estimating characteristics of large graphs via sampling is a vital part of the study of complex networks. Current sampling methods such as (independent) random vertex and random w...