Novelty Search and the Problem with Objectives

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  author =       "Joel Lehman and Kenneth O. Stanley",
  title =        "Novelty Search and the Problem with Objectives",
  booktitle =    "Genetic Programming Theory and Practice IX",
  year =         "2011",
  editor =       "Rick Riolo and Ekaterina Vladislavleva and 
                 Jason H. Moore",
  series =       "Genetic and Evolutionary Computation",
  address =      "Ann Arbor, USA",
  month =        "12-14 " # may,
  publisher =    "Springer",
  chapter =      "3",
  pages =        "37--56",
  keywords =     "genetic algorithms, genetic programming, Novelty
                 search, objective-based search, non-objective search,
                 deception, evolutionary computation",
  isbn13 =       "978-1-4614-1769-9",
  DOI =          "doi:10.1007/978-1-4614-1770-5_3",
  abstract =     "By synthesising a growing body of work in search
                 processes that are not driven by explicit objectives,
                 this paper advances the hypothesis that there is a
                 fundamental problem with the dominant paradigm of
                 objective-based search in evolutionary computation and
                 genetic programming: Most ambitious objectives do not
                 illuminate a path to themselves. That is, the gradient
                 of improvement induced by ambitious objectives tends to
                 lead not to the objective itself but instead to dead
                 end local optima. Indirectly supporting this
                 hypothesis, great discoveries often are not the result
                 of objective-driven search. For example, the major
                 inspiration for both evolutionary computation and
                 genetic programming, natural evolution, innovates
                 through an open-ended process that lacks a final
                 objective. Similarly, large-scale cultural evolutionary
                 processes, such as the evolution of technology,
                 mathematics, and art, lack a unified fixed goal. In
                 addition, direct evidence for this hypothesis is
                 presented from a recently-introduced search algorithm
                 called novelty search. Though ignorant of the ultimate
                 objective of search, in many instances novelty search
                 has counter-intuitively outperformed searching directly
                 for the objective, including a wide variety of
                 randomly-generated problems introduced in an experiment
                 in this chapter. Thus a new understanding is beginning
                 to emerge that suggests that searching for a fixed
                 objective, which is the reigning paradigm in
                 evolutionary computation and even machine learning as a
                 whole, may ultimately limit what can be achieved. Yet
                 the liberating implication of this hypothesis argued in
                 this paper is that by embracing search processes that
                 are not driven by explicit objectives, the breadth and
                 depth of what is reachable through evolutionary methods
                 such as genetic programming may be greatly expanded.",
  notes =        "part of \cite{Riolo:2011:GPTP}",
  affiliation =  "Department of EECS, University of Central Florida,
                 Orlando, Florida, USA",

Genetic Programming entries for Joel Lehman Kenneth O Stanley