Survival ratio GA for two-evaluation problem in parameter tuning of heat shock response in E. coli

In the field of bioinformatics, several studies have attempted to reconstruct biological reactions on a computer in order to understand their essence. In this study, we optimized the parameter tuning problem of the heat shock response in E. coli, one of the gene regulatory network simulations, which exhibits a transient peak by genetic algorithms (GAs). However, if the search area is too large, the optimization performance deteriorated significantly. To optimize this problem more efficiently, we defined two evaluation functions that represent two features of the simulation and used the survival ratio GA. This GA has a gene´s lifetime as a new concept, that is, the population holds previous search histories. By alternating between two evaluation functions every generation, the population holds both previously evaluated genes and children inherit both properties. In the two-evaluation problem of the heat shock response, the survival ratio GA exhibited a considerably better optimization performance than traditional GA methods.