Abandoning Objectives: Evolution through the Search for Novelty Alone

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@Article{Lehman:2011:EC,
  author =       "Joel Lehman and Kenneth O. Stanley",
  title =        "Abandoning Objectives: Evolution through the Search
                 for Novelty Alone",
  journal =      "Evolutionary Computation",
  year =         "2011",
  volume =       "19",
  number =       "2",
  pages =        "189--223",
  month =        "Summer",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00025",
  size =         "34 pages",
  abstract =     "In evolutionary computation, the fitness function
                 normally measures progress towards an objective in the
                 search space, effectively acting as an objective
                 function. Through deception, such objective functions
                 may actually prevent the objective from being reached.
                 While methods exist to mitigate deception, they leave
                 the underlying pathology untreated: Objective functions
                 themselves may actively misdirect search towards dead
                 ends. This paper proposes an approach to circumventing
                 deception that also yields a new perspective on
                 open-ended evolution: Instead of either explicitly
                 seeking an objective or modelling natural evolution to
                 capture open-endedness, the idea is to simply search
                 for behavioural novelty. Even in an objective-based
                 problem, such novelty search ignores the objective.
                 Because many points in the search space collapse to a
                 single behavior, the search for novelty is often
                 feasible. Furthermore, because there are only so many
                 simple behaviours, the search for novelty leads to
                 increasing complexity. By decoupling open-ended search
                 from artificial life worlds, the search for novelty is
                 applicable to real world problems. Counterintuitively,
                 in the maze navigation and biped walking tasks in this
                 paper, novelty search significantly outperforms
                 objective-based search, suggesting the strange
                 conclusion that some problems are best solved by
                 methods that ignore the objective. The main lesson is
                 the inherent limitation of the objective-based paradigm
                 and the unexploited opportunity to guide search through
                 other means.",
}

Genetic Programming entries for Joel Lehman Kenneth O Stanley

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