Data from: Improving HybrID: how to best combine indirect and direct encoding in evolutionary algorithms

Many challenging engineering problems are regular, meaning solutions to
one part of a problem can be reused to solve other parts. Evolutionary
algorithms with indirect encoding perform better on regular problems
because they reuse genomic information to create regular phenotypes.
However, on problems that are mostly regular, but contain some
irregularities, which describes most real-world problems, indirect
encodings struggle to handle the irregularities, hurting performance.
Direct encodings are better at producing irregular phenotypes, but cannot
exploit regularity. An algorithm called HybrID combines the best of both:
it first evolves with indirect encoding to exploit problem regularity,
then switches to direct encoding to handle problem irregularity. While
HybrID has been shown to outperform both indirect and direct encoding, its
initial implementation required the manual specification of when to switch
from indirect to direct encoding. In this paper, we test two new methods
to improve HybrID by eliminating the need to manually specify this
parameter. Auto-Switch-HybrID automatically switches from indirect to
direct encoding when fitness stagnates. Offset-HybrID simultaneously
evolves an indirect encoding with directly encoded offsets, eliminating
the need to switch. We compare the original HybrID to these alternatives
on three different problems with adjustable regularity. The results show
that both Auto-Switch-HybrID and Offset-HybrID outperform the original
HybrID on different types of problems, and thus offer more tools for
researchers to solve challenging problems. The Offset-HybrID algorithm is
particularly interesting because it suggests a path forward for
automatically and simultaneously combining the best traits of indirect and
direct encoding.