Novelty Search makes Evolvability Inevitable
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Abstract (arXiv)
Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve. The estimation of evolvability is not straightforward and is generally too expensive to be directly used as selective pressure in the evolutionary process. Indirectly promoting evolvability as a side effect of other easier and faster to compute selection pressures would thus be advantageous. In an unbounded behavior space, it has already been shown that evolvable individuals naturally appear and tend to be selected as they are more likely to invade empty behavior niches. Evolvability is thus a natural byproduct of the search in this context. However, practical agents and environments often impose limits on the reach-able behavior space. How do these boundaries impact evolvability? In this context, can evolvability still be promoted without explicitly rewarding it? We show that Novelty Search implicitly creates a pressure for high evolvability even in bounded behavior spaces, and explore the reasons for such a behavior. More precisely we show that, throughout the search, the dynamic evaluation of novelty rewards individuals which are very mobile in the behavior space, which in turn promotes evolvability.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 1.
6 papers.
- 2021Unsupervised Behaviour Discovery with Quality-Diversity Optimisationcited by 30
- 2022Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Searchcited by 9
- 2021BR-NS: an Archive-less Approach to Novelty Searchcited by 7
- 2023Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Roboticscited by 6
- 2024BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Searchcited by 2
- 2021Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithmscited by 1
- 2024BEACON: A Bayesian Optimization Inspired Strategy for Efficient Novelty Search5 Jun 2024
- 2023Quality Diversity under Sparse Reward and Sparse Interaction: Application to Grasping in Robotics10 Aug 2023
- 2022Automatic Acquisition of a Repertoire of Diverse Grasping Trajectories through Behavior Shaping and Novelty Search17 May 2022
- 2021Unsupervised Behaviour Discovery with Quality-Diversity Optimisation10 Jun 2021
- 2021Selection-Expansion: A Unifying Framework for Motion-Planning and Diversity Search Algorithms10 Apr 2021
- 2021BR-NS: an Archive-less Approach to Novelty Search8 Apr 2021