  Recent theoretical research proposes that computational complexity can be
seen as an ultimate constraint that allows for open-ended biological evolution
on finite static fitness landscapes. Whereas on easy fitness landscapes,
evolution will quickly converge to a local fitness peaks, on hard fitness
landscapes this computational constraints prevents evolution from reaching any
local fitness peak in polynomial time. Valued constraint satisfaction problems
(VCSPs) can be used to represent both easy and hard fitness landscapes. Thus
VCSPS can be seen as a natural way of linking the theory of evolution with
notions of computer science to better understand the features that make
landscapes hard. However, there are currently no simulators that study
VCSP-structured fitness landscapes.
  This report describes the design and build of an evolution simulator for
VCSP-structured fitness landscapes. The platform is used for simulating various
instances of easy and hard fitness landscapes. In particular, we look at
evolution under more realistic assumptions than fittest mutant strong-selection
weak mutation dynamics on the winding semismooth fitness landscape. The results
obtained match with the theoretical expectations, while also providing new
information about the limits of evolution. The last part of the report
introduces a mathematical model for smooth fitness landscapes and uses it to
better understand why these landscapes are easy.

Populations and Evolution;Neural and Evolutionary Computing