Chemical Genetic Algorithms—Evolutionary Optimization of Binary-to-Real-Value Translation in Genetic Algorithms

A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Not only the information in DNA, but also smaller molecules responsible for the transcription and translation of DNA into amino acids, are adaptively changed during evolution, which optimizes the fundamental mapping from binary substrings in DNA (genotype) to real values for a parameter set (phenotype). Through the struggle between cells containing a DNA unit and small molecular units, the codes (DNA) and the interpreter (the small molecular units) coevolve, and a specific output function, from which a cell's fitness is evaluated, is optimized. To demonstrate the effectiveness of the CGA, it is applied to a set of variable-separable and variable-inseparable problems, and it is shown that the CGA can robustly solve a wide range of optimization problems regardless of their fitness characteristics. To ascertain the optimization of the genotypeto-phenotype mapping by the CGA, we also conduct analytical experiments for some problems while observing the basin size of a global optimum solution in the binary genotype space. The results show that the CGA effectively augments the basin size, makes it easier for evolution to find a path to the global optimum solution, and enhances the GA's evolvability during evolution.