The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems

TL;DR

This paper investigates whether incorporating epigenetic blocking mechanisms into a multi-objective genetic algorithm improves its ability to solve dynamic optimization problems, inspired by biological epigenetic inheritance.

Contribution

It introduces an epigenetic blocking mechanism into MOEA/D-DE and demonstrates performance improvements on multiple dynamic test problems, bridging biology and evolutionary computation.

Findings

Improved performance on 12 of 16 dynamic test problems

Epigenetic mechanisms can enhance adaptability in evolutionary algorithms

Suggests further exploration of biological epigenetics in optimization methods

Abstract

Hundreds of Evolutionary Computation approaches have been reported. From an evolutionary perspective they focus on two fundamental mechanisms: cultural inheritance in Swarm Intelligence and genetic inheritance in Evolutionary Algorithms. Contemporary evolutionary biology looks beyond genetic inheritance, proposing a so-called ``Extended Evolutionary Synthesis''. Many concepts from the Extended Evolutionary Synthesis have been left out of Evolutionary Computation as interest has moved toward specific implementations of the same general mechanisms. One such concept is epigenetic inheritance, which is increasingly considered central to evolutionary thinking. Epigenetic mechanisms allow quick non- or partially-genetic adaptations to environmental changes. Dynamic multi-objective optimisation problems represent similar circumstances to the natural world where fitness can be determined by multiple objectives (traits), and the environment is constantly changing. This paper asks if the advantages that epigenetic inheritance provide in the natural world are replicated in dynamic multi-objective optimisation problems. Specifically, an epigenetic blocking mechanism is applied to a state-of-the-art multi-objective genetic algorithm, MOEA/D-DE, and its performance is compared on three sets of dynamic test functions, FDA, JY, and UDF. The mechanism shows improved performance on 12 of the 16 test problems, providing initial evidence that more algorithms should explore the wealth of epigenetic mechanisms seen in the natural world.