Chemical genetic algorithms - coevolutionary genotype-phenotype mapping by modelling of metabolism in cell

A chemical genetic algorithm (CGA) in which several types of molecules (information units) react with each other in a cell is proposed. Translation from codons (short substrings of bits) in DNA to amino acids (real value units) is specified by a particular set of translation molecules created by the reaction between RNA units and amino acid units. This adaptively changes and optimizes the fundamental genotype-phenotype mapping during evolution. Through the struggle between cells containing a DNA unit and a small molecular units, the codes in DNA and the translation table described by the small molecular coevolve, and a specific output function (protein), which is used to evaluate the cells´ fitness, is optimized. To demonstrate the effectiveness of the CGA, the algorithm is applied to a set of deceptive problems and the Shekel´s foxholes problem with epistasis, and the results by using the CGA are compared to those by using a simple GA. It is shown that the CGA becomes a powerful strategy for optimizing functions that are hard to solve with the conventional GA.