Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Darwinian Genetic Code: An Adaptation for Adapting?
Genetic Programming and Evolvable Machines
Hi-index | 0.00 |
We have studied the canonical genetic code optimality by means of simulated evolution. A genetic algorithm is used to search for better adapted hypothetical codes and as a method to guess the difficulty in finding such alternative codes. Such analysis is performed within the coevolution theory of the genetic code organization. We have studied the progression of the canonical genetic code optimality within such theory, considering a possible scenario of a previous code with two-letter codons as well as the current organization of the canonical code. Moreover, we have analysed the particular optimality and progression of adaptability of the individual nucleotide bases.