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

TL;DR

This paper introduces an epigenetic-inspired technique for evolutionary algorithms, modeling biological epigenetic regulation to enhance adaptation and learning in optimization problems.

Contribution

It presents a novel epigenetic regulation-based method for evolutionary algorithms, including a new representation, functions, and inheritance mechanisms inspired by biology.

Findings

The approach improves solution quality in optimization tasks.

The method demonstrates effective adaptation through epigenetic mechanisms.

Experimental results validate the applicability of the proposed technique.

Abstract

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.