Since their appearance in 1993, first approaching the Shannon limit, turbo codes have given a new direction in the channel encoding field, especially since they have been adopted for multiple norms of telecommunications such as deeper communication. A robust interleaver can significantly contribute to the overall performance a turbo code system. Search for a good interleaver is a complex combinatorial optimization problem. In this paper, we present genetic algorithms and differential evolution, two bio-inspired approaches that have proven the ability to solve non-trivial combinatorial optimization tasks, as promising optimization methods to find a well-performing interleaver for large frame sizes.
Matrix factorization or factor analysis is an important task helpful in the analysis of high dimensional real world data. There are several well known methods and algorithms for factorization of real data but many application areas including information retrieval, pattern recognition and data mining require processing of binary rather than real data. Unfortunately, the methods used for real matrix factorization fail in the latter case. In this paper we introduce background and initial version of Genetic Algorithm for binary matrix factorization.
This article presents an application of evolutionary fuzzy rules to the modeling and prediction of power output of a real-world Photovoltaic Power Plant (PVPP). The method is compared to artificial neural networks and support vector regression that were also used to build predictors in order to analyse a time-series like data describing the production of the PVPP. The models of the PVPP are created using different supervised machine learning methods in order to forecast the short-term output of the power plant and compare the accuracy of the prediction.