ABSTRACT
Novelty search is a recently proposed method for evolutionary computation designed to avoid the problem of deception, in which the fitness function guides the search process away from global optima. Novelty search replaces fitness-based selection with novelty-based selection, where novelty is measured by comparing an individual's behavior to that of the current population and an archive of past novel individuals. Though there is substantial evidence that novelty search can overcome the problem of deception, the critical factors in its performance remain poorly understood. This paper helps to bridge this gap by analyzing how the behavior function, which maps each genotype to a behavior, affects performance. We propose the notion of descendant fitness probability (DFP), which describes how likely a genotype's descendants are to have a certain fitness, and formulate two hypotheses about when changes to the behavior function will improve novelty search's performance, based on the effect of those changes on behavior and DFP. Experiments in both artificial and deceptive maze domains provide substantial empirical support for these hypotheses.
- P. Darwen and X. Yao. Every niching method has its niche: Fitness sharing and implicit sharing compared. In PPSN IV, volume 1141, pages 398--407, 1996. Google ScholarDigital Library
- K. A. De Jong. An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan, Ann Arbor, 1975. Google ScholarDigital Library
- F. Gomez and R. Mikkulainen. Incremental evolution of complex general behavior. In Adaptive Behavior, volume 5, pages 317--342. MIT Press, 1997. Google ScholarDigital Library
- F. J. Gomez. Sustaining diversity using behavioral information distance. In GECCO-09, pages 113--120, 2009. Google ScholarDigital Library
- G. R. Harik. Finding multimodal solutions using restricted tournament selection. In ICGA-95, pages 24--31, 1995. Google ScholarDigital Library
- J. Lehman and K. O. Stanley. Exploiting open-endedness to solve problems through the search for novelty. In ALIFE-XI, 2008.Google Scholar
- J. Lehman and K. O. Stanley. Abandoning objectives evolution through the search for novelty alone. In Evolutionary Computation. 2010. Google ScholarDigital Library
- J. Lehman and K. O. Stanley. Efficiently evolving programs through the search for novelty. In GECCO-10, pages 837--844, 2010. Google ScholarDigital Library
- J. Lehman and K. O. Stanley. Revising the evolutionary computation abstraction: minimal criteria novelty search. In GECCO -10, pages 103--110, 2010. Google ScholarDigital Library
- M. Lynch. The frailty of adaptive hypotheses for the origins of organismal complexity. Proceedings of the National Academy of Sciences, 104(Suppl 1):8597--8604, 2007.Google ScholarCross Ref
- S. W. Mahfoud. Niching methods for genetic algorithms. PhD thesis, University of Illinois, Urbana-Champaign, 1995. Google ScholarDigital Library
- T. Miconi. Evolution and complexity: the double-edged sword. Artificial life, 14(3):325--344, 2008. Google ScholarDigital Library
- J.-B. Mouret and S. Doncieux. Overcoming the bootstrap problem in evolutionary robotics using behavioral diversity. In CEC-09, pages 1161--1168, 2009. Google ScholarDigital Library
- A. Petrowski. A clearing procedure as a niching method for genetic algorithms. In IEEE IECC, pages 798--803, 1996.Google ScholarCross Ref
- S. Risi, C. Hughes, and K. Stanley. Evolving plastic neural networks with novelty search. Adaptive Behavior, 2010. Google ScholarDigital Library
- S. Risi, S. D. Vanderbleek, C. Hughes, and K. O. Stanley. How novelty search escapes the deceptive trap of learning to learn. In GECCO-09, pages 153--160, 2009. Google ScholarDigital Library
- B. Sareni and L. Krahenbuhl. Fitness sharing and niching methods revisited. Evolutionary Computation, IEEE Transactions on, 2(3):97--106, 1998. Google ScholarDigital Library
- K. O. Stanley and R. Miikkulainen. Evolving neural networks through augmenting topologies. Evolutionary Computation, 10(2):99--127, 2002. Google ScholarDigital Library
- E. Uchibe, M. Yanase, and M. Asada. Behavior generation for a mobile robot based on the adaptive fitness function. Robotics and Autonomous Systems, 40:69--77(9), 2002.Google ScholarCross Ref
- J. Urzelai, D. Floreano, M. Dorigo, and M. Colombetti. Incremental robot shaping. Connection Science, 10:341--360(20), 1998.Google ScholarCross Ref
- D. L. Whitley. Fundamental principles of deception in genetic search. In Foundations of Genetic Algorithms, pages 221--241. Morgan Kaufmann, 1991.Google Scholar
- A. P. Wieland. Evolving neural network controllers for unstable systems. In IJCNN-91, pages 667--673, 1991.Google ScholarCross Ref
Index Terms
- Critical factors in the performance of novelty search
Recommendations
Devising Effective Novelty Search Algorithms: A Comprehensive Empirical Study
GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary ComputationNovelty search is a state-of-the-art evolutionary approach that promotes behavioural novelty instead of pursuing a static objective. Along with a large number of successful applications, many different variants of novelty search have been proposed. It ...
Novelty search: a theoretical perspective
GECCO '19: Proceedings of the Genetic and Evolutionary Computation ConferenceNovelty Search is an exploration algorithm driven by the novelty of a behavior. The same individual evaluated at different generations has different fitness values. The corresponding fitness landscape is thus constantly changing and if, at the scale of ...
Novelty-driven cooperative coevolution
Cooperative coevolutionary algorithms CCEAs rely on multiple coevolving populations for the evolution of solutions composed of coadapted components. CCEAs enable, for instance, the evolution of cooperative multiagent systems composed of heterogeneous ...
Comments