This paper develops an algorithm that extracts explanatory rules from microarray data, which we treat as time series, using genetic programming (GP) and fuzzy logic. Reverse polish notation is used (RPN) to describe the rules and to facilitate the GP approach. The algorithm also allows for the insertion of prior knowledge, making it possible to find sets of rules that include the relationships between genes already known. The algorithm proposed is applied to problems arising in the construction of gene regulatory networks, using two different sets of real data from biological experiments on the Arabidopsis thaliana cold response and the rat central nervous system, respectively. The results show that the proposed technique can fit data to a pre-defined precision even in situations where the data set has thousands of features but only a limited number of points in time are available, a situation in which traditional statistical alternatives encounter difficulties, due to the scarcity of time points.

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This paper describes genetic algorithms that use the Calisnki-Harabasz index as its evaluation function and graphs techniques that are both used to identify patterns in cryptograms generated by cryptographics algorithms certified by NIST (National Institute Standard Technology), namely AES, RC6, MARS, Twofish and Serpent. Evidence of patterns or "signatures" generated by the algorithms under test were detected, thus corroborating the results of other studies quoted here. The results obtained with these two techniques are compared with results reported in [4] and [5], showing superiority in the accuracy of class generation.