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In some other articles in this blog, I have already written about complex time series, recurrence quantification analysis (RQA) and neural networks. In this series of articles, I will discuss some points to take into account when combining the use of these two tools to identify patterns in complex series, such as detecting anomalies in electrocardiograms or electroencephalograms.

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With this article I conclude the series dedicated to the application of genetic algorithms to the design of neural networks. I will explain the most relevant code of the sample application given with these articles, mainly the classes dedicated to the genes management and the selection process. You can find more information in the previous articles of the series.

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In this second article on the application of evolutionary algorithms to the optimization of the design of neural networks, I am going to provide a small sample application that allows you to build and train networks, in addition to using this type of algorithms to find the best configuration for a given data set. The application allows generating artificial test data, and I provide the source code for you to be able to modify it, as you want.

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When we try to apply a neural network to a given problem, finding the most suitable topology for it can be a tedious trial and error task, as well as end up producing a poorly optimized network. To automate this process, we can draw on evolutionary algorithms, inspired in the natural selection of living organisms, which can greatly facilitate our job.

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One of the most popular algorithms for training multilayer artificial neural networks is the back propagation algorithm, or retro-propagation algorithm. In this article I will try to explain its fundamentals, through a simplified implementation of a neural network that allows testing with different configurations of the network.

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The Tableaux is a logical calculation tool that allows checking the validity of a conclusion from a series of premises. In the previous article I explained briefly the fundamentals of predicate logic Tableaux. In this article I will extend the program to the first-order logic, which has much more expressive capacity than predicate logic, although this makes also automatic calculation more difficult.

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The Tableaux is a logical calculus tool that is very useful to demonstrate a conclusion from a set of premises, find counterexamples or models of a set of logical formulas or demonstrate that a formula is a tautology, i.e., that is true in all possible cases. They can be used in artificial intelligence as the basis for implementing automated theorem proving.

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