Virtual Ecosystems - Evolutionary and Genetic Programming from the perspective of modern means of ecosystem-modelling

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Clemens Frey",
  title =        "Virtual Ecosystems - Evolutionary and Genetic
                 Programming from the perspective of modern means of
  publisher =    "Institute for Terrestrial Ecosystems, Bayreuth",
  year =         "2002",
  volume =       "93",
  series =       "Bayreuth Forum Ecology",
  address =      "Bayreuth, Germany",
  note =         "(in German)",
  email =        "",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0944-4122",
  URL =          "",
  broken =       "",
  size =         "199 p.",
  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.",
  abstract =     "The following chapter addresses the issue of
                 artificially creating evolution in virtual or simulated
                 ecosystems and the question whether this can be done
                 with the help of Evolutionary Computation. Since we
                 want to analyse shortcomings of the conventional
                 approaches and necessary adjustments, basic features of
                 natural evolution are stated and discussed at first.
                 Then we take a closer look at the area of Artificial
                 Life and discuss specific software from this field.
                 This discussion is concerned with so-called strong
                 approaches like tierra and avida as well as weak
                 approaches like the ecosystem-oriented Tragic++ system;
                 besides, connections to social learning paradigms and
                 Nouveau Artificial Intelligence are highlighted. Taking
                 this broad view into account we conclude this chapter
                 by listing a set of features which have to be comprised
                 by a serious a model for evolution in virtual
                 ecosystems. The gist of these desired features says
                 that it is feasible to represent strategy programs as
                 trees like in Genetic Programming, for this kind of
                 representation causes a non-trivial, morphogenic
                 mapping between the genotypic and the phenotypic space.
                 It has to be conceded, however, that exogenously and
                 a-priori given fitness-functions as well as the
                 synchronous reproduction schemes which are almost
                 always used in Genetic Programming are not appropriate
                 for modeling evolution in virtual ecosystems. Chapters
                 four to six describe how a system called MathEvEco was
                 formulated and implemented according to these
                 guidelines. Chapter four focuses on strongly typed tree
                 representations of programs. Feasible sets of strongly
                 typed program trees are defined precisely and their
                 relationship with context-free grammars and the
                 parameter-dependent evaluation of program trees are
                 investigated in mathematical terms. These mathematical
                 tools having been made available, genetic operators and
                 initialization procedures of MathEvEco are stringently
                 formulated in the fifth chapter. The system was
                 supposed to be as flexible as possible. To this end the
                 author has not only accessed a strongly typed version
                 of the very classic crossover operator, but included a
                 bunch of strongly typed mutation operators and the
                 novel PTC2 algorithm for randomly generating program
                 trees. In order to allow algorithmic comparisons the
                 operators may be assembled in two fundamentally
                 different ways; they may either be merged into a system
                 of common Genetic Programming or they may be assembled
                 as the desired system for modeling evolution in virtual
                 ecosystems. Both of these possibilities are described,
                 still in mathematical terms.The resulting systems are
                 called MathEvEco-GP and MathEvEco-AL, respectively.",
  abstract =     "While chapter five has been written in order to allow
                 these systems to be communicated in a transparent and
                 precise manner, chapter six shall illuminate their
                 actual implementation within the scope of the
                 mathematical software system Mathematica. To this end
                 we show how program trees are handled in Mathematica,
                 how model-specific and problem-specific knowledge is to
                 be inserted by the user of MathEvEco, and in which way
                 the various genetic operators have been implemented.
                 Since MathEvEco can not only be run on a single machine
                 but rather on clusters of workstations, there is a
                 special treatment of aspects of parallel programming,
                 too. Finally the functionality of MathEvEco is
                 exemplified by means of a symbolic regression
                 problem.The final chapter seven is dedicated to a case
                 study. It consists of automatically generating search
                 devices which is a special case of the general setting
                 having been introduced in chapter one. There are a two
                 different interpretations of this special problem. On
                 the one hand side it may be understood in terms of
                 numerical optimization; we presuppose an multi-modal
                 objective function which may be imagined as a
                 three-dimensional surface having many peaks. Strategies
                 have to be evolved by MathEvEco-GP which are only
                 provided with local information about this surface but
                 are nevertheless required to lead the search devices to
                 one of the highest peaks. On the other hand side the
                 special problem may be understood in terms of an
                 ecosystem where many organisms struggle for allocating
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Genetic Programming entries for Clemens Frey