IEEE Computational Intelligence Magazine - November 2021 - 99

the best fitnesses found in both cases are
quite close. Consequently, the bloat
effect is obvious for the addition of two
types of NPs per step, but the same can
not be said for the case of adding one
type of NPs.
VII Conclusion
The effects of genome length are studied
here both on an abstract model and
a real-life problem of designing a NPbased
anti-cancer treatment. Firstly, the
influence that the ruggedness of the fitness
function landscape has on the
genome length through evolution is
investigated with the abstract NK
model. Growth is observed, with the
expansion of genome lengths not
obstructed by the ruggedness of the fitness
landscape. On the contrary, the
expansion of genome lengths can be
encouraged by the topology of such
landscapes, where typical peaks of low
amplitude increase the possibility of
higher fitness outcome per the added
randomly generated sequence. It is noteworthy
that no specific advantage is
implemented in the abstract model for
larger lengths of genomes, thus the
observed limited growth (contrast to
what happens during bloat situations) is
explicitly due to the inherent nature of
evolution over rugged fitness landscapes.
Then, by optimizing the design of
NP drug-delivery systems in a cancer
simulator, we investigate the increase of
the genome length in a real-world problem.
Despite the fact that no indication
of the best treatment composition (or
the number of different types of NPs) is
included in the model, evolved solutions
converge to treatments with eight different
types of NPs, for the method that
adds one type of NPs per step. For the
method that adds two types of NPs per
step, evolved solutions converge to
slightly more complex treatments (i.e., 9
types of NPs). This general behavior of
higher growth with larger sequences
added correlates well with observed
behavior in the NK model. Moreover, as
deduced here and by using other versions
of the NK model (after [24]), the
gradual growth through small step
increases in genome length appears
more appropriate in the application
domain. That is, whilst the fitness of the
solutions found is quite similar, the
higher complexity of NP-based cancer
treatment drug delivery systems is harder
to produce, will probably prove to be
more toxic, and has the greater potential
for unintended consequences when
used in vivo.
Acknowledgment
This work was supported by the European
Research Council under the European
Union's Horizon 2020 research
and innovation program under grant
agreement No. 800983.
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NOVEMBER 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 99
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