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This work presents the experimental results obtained with a distributed computing system created by mapping an evolutionary algorithm to the CouchDB object store. The framework decouples the population from the evolutionary algorithm and-through the API that CouchDB provides-allows the distributed and asynchronous operation of clients written in different programming languages. In this paper we present tests which prove that the novel algorithm design still performs as good as a canonical evolutionary algorithm and discover what are the main issues concerning it, what kind of speedups should we expect, and how all this affects the fundamental evolutionary algorithms concepts.

The spread of multiprocessor and multi-core architectures have a pervasive effect on the way software is developed. In order to take full advantage of them, a parallel implementation of every single program would be needed, but also a radical reformulation of the algorithms that are more appropriate to that kind of implementation. In this work we design and implement an evolutionary computation model using programming languages with built-in concurrent concepts. This article shows the advantages of these paradigms in order to implement a parallel genetic algorithm (pGA) with an island pools based topology in the concurrent-functional oriented programming languages: Erlang, Scala, and Clojure. Some implementation decisions are analyzed and the results of the solution of a study case are shown.

In this paper we describe how the usual sequential and procedural Evolutionary Algorithm is mapped to a concurrent and functional framework using the Erlang language. The design decisions, as well as some early results, are shown.

An associative memory is a system that relates input patterns and output patterns, furthermore is able to recover the output vector associated although the input pattern was contaminated by some kind of noise. Alpha Beta associative memories are robust to subtractive and additive noise and are one of the fastest associative memories besides other qualities. In this paper we show a way to reduce the number of operations in the learning phase. The operation alpha used in the learning phase allow us to propose 8 theorems; with those theorems is possible to construct an alternative learning method. By this method, the number of alpha operations needed to learning each pattern is reduced and replaced by assignations, furthermore we also eliminate the min and max operations. This reduces the learning time drastically with either big dimension patterns or a big number of patterns.