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.

本文研究了在动态多目标优化问题中应用表观遗传机制对现有多目标遗传算法 MOEA/D-DE 的性能进行了比较，并提供了初步的实验证据表明，更多的算法应该探索自然界中丰富的表观遗传机制。