Evolution Through the Search for Novelty

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@PhdThesis{Lehman:thesis,
  author =       "Joel Lehman",
  title =        "Evolution Through the Search for Novelty",
  school =       "Department of Electrical Engineering and Computer
                 Science in the College of Engineering and Computer
                 Science at the University of Central Florida",
  year =         "2012",
  address =      "Orlando, Florida, USA",
  month =        "Summer Term",
  keywords =     "genetic algorithms, genetic programming, Novelty
                 search",
  URL =          "http://joellehman.com/lehman-dissertation.pdf",
  URL =          "http://purl.fcla.edu/fcla/etd/CFE0004398",
  size =         "223 pages",
  abstract =     "I present a new approach to evolutionary search called
                 novelty search, wherein only behavioural novelty is
                 rewarded, thereby abstracting evolution as a search for
                 novel forms. This new approach contrasts with the
                 traditional approach of rewarding progress towards the
                 objective through an objective function. Although they
                 are designed to light a path to the objective,
                 objective functions can instead deceive search into
                 converging to dead ends called local optima.

                 As a significant problem in evolutionary computation,
                 deception has inspired many techniques designed to
                 mitigate it. However, nearly all such methods are still
                 ultimately susceptible to deceptive local optima
                 because they still measure progress with respect to the
                 objective, which this dissertation will show is often a
                 broken compass. Furthermore, although novelty search
                 completely abandons the objective, it counter
                 intuitively often out-performs methods that search
                 directly for the objective in deceptive tasks and can
                 induce evolutionary dynamics closer in spirit to
                 natural evolution. The main contributions are to (1)
                 introduce novelty search, an example of an effective
                 search method that is not guided by actively measuring
                 or encouraging objective progress; (2) validate novelty
                 search by applying it to biped locomotion; (3)
                 demonstrate novelty search's benefits for evolvability
                 (i.e. the ability of an organism to further evolve) in
                 a variety of domains; (4) introduce an extension of
                 novelty search called minimal criteria novelty search
                 that brings a new abstraction of natural evolution to
                 evolutionary computation (i.e. evolution as a search
                 for many ways of meeting the minimal criteria of life);
                 (5) present a second extension of novelty search called
                 novelty search with local competition that abstracts
                 evolution instead as a process driven towards diversity
                 with competition playing a subservient role; and (6)
                 evolve a diversity of functional virtual creatures in a
                 single run as a culminating application of novelty
                 search with local competition. Overall these
                 contributions establish novelty search as an important
                 new research direction for the field of evolutionary
                 computation.",
  notes =        "Major Professor: Kenneth O. Stanley

                 Public - Allow Worldwide Access CFE0004398 Graduation
                 Date 2012-08-01 Release Date 2012-08-15

                 Chapter 5 on GP, Santa Fe Ant",
}

Genetic Programming entries for Joel Lehman

Citations