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BCIT Citations Collection
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- Design of a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations
- A new approach to design a dynamic model of genes with multiple autonomous regulatory modules by evolutionary computations is proposed. The approach is based on Genetic Algorithms (GA), with new crossover operators especially designed for these purposes. The new operators use local homology between parental strings to preserve building blocks found by the algorithm. The approach exploits the subbasin-portal architecture of the fitness functions suitable for this kind of evolutionary modeling. This architecture is significant for Royal Road class fitness functions. Two real-life Systems Biology problems with such fitness functions are implemented here: evolution of the bacterial promoter rrnP1 and of the enhancer of the Drosophila even-skipped gene. The effectiveness of the approach compared to standard GA is demonstrated on several benchmark and reallife tasks., Peer-reviewed article, Published.
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- Retroviral genetic algorithms
- Proceedings of the 2011 International Conference on Evolutionary Computation Theory and Applications. Classical understandings of biological evolution inspired creation of the entire order of Evolutionary Computation (EC) heuristic optimization techniques. In turn, the development of EC has shown how living organisms use biomolecular implementations of these techniques to solve particular problems in survival and adaptation. An example of such a natural Genetic Algorithm (GA) is the way in which a higher organism's adaptive immune system selects antibodies and competes against its complement, the development of antigen variability by pathogenic organisms. In our approach, we use operators that implement the reproduction and diversification of genetic material in a manner inspired by retroviral reproduction and a genetic-engineering technique known as DNA shuffling. We call this approach Retroviral Genetic Algorithms, or retroGA (Spirov and Holloway, 2010). Here, we extend retroGA to include: (1) the utilization of tags in strings; (2) the capability of the Reproduction-Crossover operator to read these tags and interpret them as instructions; and (3), as a consequence, to use more than one reproductive strategy. We validated the efficacy of the extended retroGA technique with benchmark tests on concatenated trap functions and compared these with Royal Road and Royal Staircase functions., Conference paper, Published.