Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
GUEST EDITORIAL: Computational intelligence in solving bioinformatics problems
Artificial Intelligence in Medicine
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In several cases, the DNA sequences of an organism are available in different stages of its evolution and it is desirable to reconstruct the DNA sequence in a previous evolution stage for which the exact sequence is not known. A CAD tool for backtracking the DNA sequence evolution based on Cellular Automata (CA) and Genetic Algorithms (GAs) was developed. Furthermore, the proposed system is able of automatic production of synthesizable VHDL code corresponding to the CA model. More specifically, DNA is modeled as a one-dimensional CA with four states per cell, i.e. the four DNA bases A, C, T and G. Linear evolution rules, represented by square matrices, are considered. The evolution rule can be determined using the global state of the DNA sequence in various evolution steps. This determination is accomplished using GAs. Moreover, because of the final produced CA's binary states and its local rule simplicity, the hardware implementation of the proposed model is straightforward. Finally, the FPGA processor that executes the CA model was fully designed, placed and routed.