Abandoning Objectives: Evolution Through the Search for Novelty Alone
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Abstract
In evolutionary computation, the fitness function normally measures progress toward an objective in the search space, effectively acting as an objective function. Through deception, such objective functions may actually prevent the objective from being reached. While methods exist to mitigate deception, they leave the underlying pathology untreated: Objective functions themselves may actively misdirect search toward dead ends. This paper proposes an approach to circumventing deception that also yields a new perspective on open-ended evolution. Instead of either explicitly seeking an objective or modeling natural evolution to capture open-endedness, the idea is to simply search for behavioral novelty. Even in…
Citation impact
887
total citations
- FWCI
- 42.40
- Percentile
- 100%
- References
- 115
Citations per year
Authors
2Topics & keywords
Topics
Keywords
- Novelty
- Deception
- Computer science
- Artificial intelligence
- Fitness function
- Function (biology)
- Machine learning
- Psychology
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