Book,
Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling
Bayreuth Forum Ecology Institute for Terrestrial Ecosystems, Bayreuth, Bayreuth, Germany, (2002)(in German).
Abstract
The realm of Evolutionary Computation covers many
tools commonly used for solving complex discrete and
continuous global optimization problems. These methods,
which are known as Genetic Algorithms, Evolution
Strategies, Evolutionary Programming and Genetic
Programming, stem from efforts of modeling adaptive
systems, from engineering and computer science. They
are based on the idea of restating the Darwinian
principles of natural evolution in algorithmic terms in
order to get problem-solving methods for non-biological
applications. Today Genetic Algorithms, Evolution
Strategies and Evolutionary Programming mainly serve as
mathematical techniques of numerical optimization.
Genetic Programming likewise is an adaptation
technique, but there is a different focus: based on
evolutionary principles Genetic Programming enables us
to automatically generate computer programs.The
underlying hypotheses of this book is that the main
point of natural, biological evolution is its data
processing aspect. Evolution is seen as a certain way
of processing individuals' and populations' genetic
data. Referring to Evolutionary Computation there is a
very interesting question now: Is it appropriate to
employ Genetic Programming and similar algorithms in
order to investigate natural evolution? Of course this
means turning around the application profile of
Evolutionary Computation, so where do we have to alter
its algorithmic structure and the like? Finally,
supposed there is a modified method, how do the results
of both the classic algorithm and the modified variant
compare to each other?In the first chapter we state the
general notion of a search strategy. It may be a living
being's policy of resource allocation, for example, but
the notion covers optimization methods, too. A search
strategy shall be defined in mathematical terms as
being a dynamical system combined with a quality
measure which is based on the trajectories the
dynamical system produces. The author proposes a
precise formulation for what a search strategy is
generally claimed to accomplish, namely to generate
dynamic behavior which gets us to the neighborhood of a
predefined goal, possibly obeying certain constraints
within the dynamics of the search process.Chapter two
contains a gentle introduction into the field of
Evolutionary Computation, namely Adaptive Systems,
Genetic Algorithms, Evolution Strategies and
Evolutionary Programming. We focus on Genetic
Programming, however, and take a look at a paradigmatic
experiment for automatically finding search strategies,
i.e. the so-called artificial ant-experiment. In doing
so the reader is also shown how the mathematical
framework built in the first chapter may be used to
formulate the artificial ant-problem.
