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Novel Object Exploration as a Potential Assay for Higher Order Repetitive Behaviors in Mice
08:28

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Published on: August 20, 2016

Abandoning objectives: evolution through the search for novelty alone.

Joel Lehman1, Kenneth O Stanley

  • 1School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, Florida 32816, USA. jlehman@eecs.ucf.edu

Evolutionary Computation
|September 28, 2010
PubMed
Summary
This summary is machine-generated.

Searching for behavioral novelty, not objectives, can overcome deception in evolutionary computation. This approach, novelty search, surprisingly outperforms objective-based methods in complex tasks.

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Area of Science:

  • Evolutionary Computation
  • Artificial Intelligence
  • Search Algorithms

Background:

  • Objective functions in evolutionary computation can be deceptive, hindering progress toward goals.
  • Existing methods to mitigate deception do not address the root cause of misdirection.
  • Objective functions may actively lead search algorithms toward suboptimal or unreachable solutions.

Purpose of the Study:

  • To propose a novel approach to circumvent deception in evolutionary computation.
  • To introduce a new perspective on open-ended evolution by focusing on behavioral novelty.
  • To demonstrate the applicability of novelty search to real-world problems.

Main Methods:

  • Implementing novelty search, which prioritizes discovering new behaviors over reaching a predefined objective.
  • Applying novelty search to maze navigation and biped walking tasks.
  • Comparing the performance of novelty search against traditional objective-based search methods.

Main Results:

  • Novelty search effectively circumvents deception inherent in objective functions.
  • The search for novelty naturally leads to increasing complexity in solutions.
  • Novelty search significantly outperformed objective-based search in maze navigation and biped walking tasks.

Conclusions:

  • Objective-based search paradigms have inherent limitations that can be overcome by alternative guidance methods.
  • Searching for behavioral novelty offers a powerful alternative to objective-driven search, even in objective-based problems.
  • Decoupling search from artificial life contexts makes novelty search broadly applicable to diverse real-world challenges.