  Many biological processes have been the source of inspiration for heuristic
methods that generate high-quality solutions to solve optimization and search
problems. This thesis presents an epigenetic technique for Evolutionary
Algorithms, inspired by the epigenetic regulation process, a mechanism to
better understand the ability of individuals to adapt and learn from the
environment. Epigenetic regulation comprises biological mechanisms by which
small molecules, also known as epigenetic tags, are attached to or removed from
a particular gene, affecting the phenotype. Five fundamental elements form the
basis of the designed technique: first, a metaphorical representation of
Epigenetic Tags as binary strings; second, a layer on chromosome top structure
used to bind the tags (the Epigenotype layer); third, a Marking Function to
add, remove, and modify tags; fourth, an Epigenetic Growing Function that acts
like an interpreter, or decoder of the tags located over the alleles, in such a
way that the phenotypic variations can be reflected when evaluating the
individuals; and fifth, a tags inheritance mechanism. A set of experiments are
performed for determining the applicability of the proposed approach.
