  Memetic Computing is a subject in computer science which considers complex
structures as the combination of simple agents, memes, whose evolutionary
interactions lead to intelligent structures capable of problem-solving. This
paper focuses on Memetic Computing optimization algorithms and proposes a
counter-tendency approach for algorithmic design. Research in the field tends
to go in the direction of improving existing algorithms by combining different
methods or through the formulation of more complicated structures. Contrary to
this trend, we instead focus on simplicity, proposing a structurally simple
algorithm with emphasis on processing only one solution at a time. The proposed
algorithm, namely Three Stage Optimal Memetic Exploration, is composed of three
memes; the first stochastic and with a long search radius, the second
stochastic and with a moderate search radius and the third deterministic and
with a short search radius. The bottom-up combination of the three operators by
means of a natural trial and error logic, generates a robust and efficient
optimizer, capable of competing with modern complex and computationally
expensive algorithms. This is suggestive of the fact that complexity in
algorithmic structures can be unnecessary, if not detrimental, and that simple
bottom-up approaches are likely to be competitive is here invoked as an
extension to Memetic Computing basing on the philosophical concept of Ockham's
Razor. An extensive experimental setup on various test problems and one digital
signal processing application is presented. Numerical results show that the
proposed approach, despite its simplicity and low computational cost displays a
very good performance on several problems, and is competitive with
sophisticated algorithms representing the-state-of-the-art in computational
intelligence optimization.
