Using Base Position Errors in an Entropy-Based Evaluation Function for the Study of Genetic Code Adaptability

The canonical genetic code is almost universal. An intriguing question is why the canonical genetic code is used instead of another genetic code. Some researchers have proposed that the canonical genetic code is a product of natural selection. This hypothesis is supported by its robustness against mutations. In this paper, we propose a new evaluation function based on entropy and robustness against base position errors for the study of genetic code adaptability. In order to find the best hypothetical genetic codes in the search space, we use a genetic algorithm (GA). The experimental results indicate that, when the proposed evaluation function is compared to the standard evaluation function based only on robustness, the difference between the fitness of the best hypothetical codes found by the GA and the fitness of the canonical genetic code is smaller.