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Effective and Fast Optimization for a Dynamic Model of the Drosophila Circadian Oscillator

In the field of systems biology, biochemical networks are being reconstructed in computer to understand their dynamic features. In this study, we focused on the estimation problem for a dynamic model of the Drosophila circadian oscillator, which is defined by two evaluation functions that represent oscillatory features. However, since the search space is multimodal and logarithmically large, it is quite difficult for ordinary GAs to optimize this evaluation problem. Therefore, we had used two-step optimizing, a random search with GA. It successfully optimized the circadian oscillator, but required a long calculation time, causing a local search. On the other hand, to optimize two evaluation functions simultaneously, we proposed the survival ratio GA, where genes have lifetimes and a population holds search histories. By alternating two evaluation functions every generation, the population holds previously evaluated genes and children inherit each and mixed properties. The survival ratio GA exhibited wider space search and higher success ratio, thus we applied the one-step optimizing with the survival ratio GA to the circadian estimation problem, which shortens the total calculation times and finds out more local optimums than the previous two-step optimizing method.