  Evolutionary Computation is a group of biologically inspired algorithms used
to solve complex optimisation problems. It can be split into Evolutionary
Algorithms, which take inspiration from genetic inheritance, and Swarm
Intelligence algorithms, that take inspiration from cultural inheritance.
However, recent developments have focused on computational or mathematical
adaptions, leaving their biological roots behind. This has left much of the
modern evolutionary literature relatively unexplored.
  To understand which evolutionary mechanisms have been considered, and which
have been overlooked, this paper breaks down successful bio-inspired algorithms
under a contemporary biological framework based on the Extended Evolutionary
Synthesis, an extension of the classical, genetics focussed, Modern Synthesis.
The analysis shows that Darwinism and the Modern Synthesis have been
incorporated into Evolutionary Computation but that the Extended Evolutionary
Synthesis has been broadly ignored beyond:cultural inheritance, incorporated in
the sub-set of Swarm Intelligence algorithms, evolvability, through CMA-ES, and
multilevel selection, through Multi-Level Selection Genetic Algorithm.
  The framework shows a missing gap in epigenetic inheritance for Evolutionary
Computation, despite being a key building block in modern interpretations of
how evolution occurs. Epigenetic inheritance can explain fast adaptation,
without changes in an individual's genotype, by allowing biological organisms
to self-adapt quickly to environmental cues, which, increases the speed of
convergence while maintaining stability in changing environments. This leaves a
diverse range of biologically inspired mechanisms as low hanging fruit that
should be explored further within Evolutionary Computation.
