Geometry of evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Alberto Moraglio",
  title =        "Geometry of evolutionary algorithms",
  booktitle =    "GECCO 2011 Tutorials",
  year =         "2011",
  editor =       "Darrell Whitley",
  isbn13 =       "978-1-4503-0690-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1439--1468",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Dublin, Ireland",
  DOI =          "doi:10.1145/2001858.2002144",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The various flavors of Evolutionary Algorithms look
                 very similar when cleared of algorithmically irrelevant
                 differences such as domain of application and phenotype
                 interpretation. Representation-independent algorithmic
                 characteristics like the selection scheme can be freely
                 exchanged between algorithms. Ultimately, the origin of
                 the differences of the various flavors of Evolutionary
                 Algorithms is rooted in the solution representation and
                 relative genetic operators. Are these differences only
                 superficial? Is there a deeper unity encompassing all
                 Evolutionary Algorithms beyond the specific
                 representation? Is a general mathematical framework
                 unifying search operators for all solution
                 representations at all possible? The aim of the
                 tutorial is to introduce a formal, but intuitive,
                 unified point of view on Evolutionary Algorithms across
                 representations based on geometric ideas, which
                 provides a possible answer to the above questions. It
                 also presents the benefits for both theory and practice
                 brought by this novel perspective.

                 The key idea behind the geometric framework is that
                 search operators have a dual nature. The same search
                 operator can be defined (i) on the underlying solution
                 representations and, equivalently, (ii) on the
                 structure of the search space by means of simple
                 geometric shapes, like balls and segments. These shapes
                 are used to delimit the region of space that includes
                 all possible offspring with respect to the location of
                 their parents. The geometric definition of a search
                 operator is of interest because it can be applied -
                 unchanged - to different search spaces associated with
                 different representations. This, in effect, allows us
                 to define exactly the same search operator across
                 representations in a rigorous way.

                 The geometric view on search operators has a number of
                 interesting consequences of which this tutorial will
                 give a comprehensive overview. These include (i) a
                 straightforward view on the fitness landscape seen by
                 recombination operators, (ii) a formal unification of
                 many pre-existing search operators across
                 representations, (iii) a principled way of designing
                 crossover operators for new representations, (iv) a
                 principled way of generalising search algorithms from
                 continuous to combinatorial spaces, and (v) the
                 potential for a unified theory of evolutionary
                 algorithms across representations.",
  notes =        "Also known as \cite{2002144} Distributed on CD-ROM at

                 ACM Order Number 910112.",

Genetic Programming entries for Alberto Moraglio