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Many biological processes have been the source of inspiration for heuristicmethods that generate high-quality solutions to solve optimization and searchproblems. This thesis presents an epigenetic technique for EvolutionaryAlgorithms, inspired by the epigenetic regulation process, a mechanism tobetter understand the ability of individuals to adapt and learn from theenvironment. Epigenetic regulation comprises biological mechanisms by whichsmall molecules, also known as epigenetic tags, are attached to or removed froma particular gene, affecting the phenotype. Five fundamental elements form thebasis of the designed technique: first, a metaphorical representation ofEpigenetic Tags as binary strings; second, a layer on chromosome top structureused to bind the tags (the Epigenotype layer); third, a Marking Function toadd, remove, and modify tags; fourth, an Epigenetic Growing Function that actslike an interpreter, or decoder of the tags located over the alleles, in such away that the phenotypic variations can be reflected when evaluating theindividuals; and fifth, a tags inheritance mechanism. A set of experiments areperformed for determining the applicability of the proposed approach.

Modeling epigenetic evolutionary algorithms: An approach based on the epigenetic regulation process