Evolution of Complexity in Real-World Domains

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Pablo Funes",
  title =        "Evolution of Complexity in Real-World Domains",
  school =       "Computer Science, Brandeis University",
  year =         "2001",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, AI",
  URL =          "http://www.demo.cs.brandeis.edu/papers/funes_phd.pdf",
  URL =          "http://www.demo.cs.brandeis.edu/papers/funes_phd.ps",
  URL =          "http://www.demo.cs.brandeis.edu/papers/funes_phd.html",
  URL =          "http://phdtree.org/pdf/23531523-evolution-of-complexity-in-real-world-domains/",
  size =         "167 pages",
  abstract =     "Artificial Life research brings together methods from
                 Artificial Intelligence (AI), philosophy and biology,
                 studying the problem of evolution of complexity from
                 what we might call a constructive point of view, trying
                 to replicate adaptive phenomena using computers and
                 robots. Here we wish to shed new light on the issue by
                 showing how computer-simulated evolutionary learning
                 methods are capable of discovering complex emergent
                 properties in complex domains. Our stance is that in AI
                 the most interesting results come from the interaction
                 between learning algorithms and real domains, leading
                 to discovery of emergent properties, rather than from
                 the algorithms themselves.

                 The theory of natural selection postulates that
                 generate-test-regenerate dynamics, exemplified by life
                 on earth, when coupled with the kinds of environments
                 found in the natural world, have lead to the appearance
                 of complex forms. But artificial evolution methods,
                 based on this hypothesis, have only begun to be put in
                 contact with real-world environments.

                 In the present thesis we explore two aspects of
                 real-world environments as they interact with an
                 evolutionary algorithm. In our first experimental
                 domain (chapter 2) we show how structures can be
                 evolved under gravitational and geometrical
                 constraints, employing simulated physics. Structures
                 evolve that exploit features of the interaction between
                 brick-based structures and the physics of gravitational

                 In a second experimental domain (chapter 3) we study
                 how a virtual world gives rise to co-adaptation between
                 human and agent species. In this case we look at the
                 competitive interaction between two adaptive species.
                 The purely reactive nature of artificial agents in this
                 domain implies that the high level features observed
                 cannot be explicit in the genotype but rather, they
                 emerge from the interaction between genetic information
                 and a changing domain.

                 Emergent properties, not obvious from the lower level
                 description, amount to what we humans call complexity,
                 but the idea stands on concepts which resist
                 formalisation -- such as difficulty or complicatedness.
                 We show how simulated evolution, exploring reality,
                 finds features of this kind which are preserved by
                 selection, leading to complex forms and behaviours. But
                 it does so without creating new levels of abstraction
                 -- thus the question of evolution of modularity remains

Genetic Programming entries for Pablo J Funes