Abstract
A novel discovery is one which is both useful and surprising: a generative model's output is a useful discovery if it has a low probability of being generated (it's surprising) and a high reward (it's useful). Global optimization can directly increase the probability of sampling high rewards but typically requires updating model weights. Such gradient based optimization is expensive and bars using capable closed-source models. Instead, modern search methods for discovery sacrifice the global target, and use evolutionary algorithms with local reward maximizing objectives, permitting the search to focus only on high probability samples. In this paper, we interpret various evolutionary algorithms as approximate Markov Chain Monte Carlo, an optimization-free method to sample from complex distributions. This interpretation allows developing Distribution Matching Evolutionary Algorithms (DME), a class of search methods which sample from a global target distribution without updating weights. Empirically, DME has a higher sample efficiency than existing methods on problems requiring many samples to find a solution.
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Publication details
- Journal
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- Open access
- Green open access
Cite this article
APA 7
Gideoni, Y., & Gal, Y. (2026). Distribution Matching Evolutionary Algorithms for Rare Event Sampling. https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling
MLA 9
Gideoni, Yonatan, and Yarin Gal. "Distribution Matching Evolutionary Algorithms for Rare Event Sampling." https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling.
Chicago (author–date)
Gideoni, Yonatan, and Yarin Gal. 2026. "Distribution Matching Evolutionary Algorithms for Rare Event Sampling." https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling.
Harvard
Gideoni, Y. and Gal, Y. (2026) 'Distribution Matching Evolutionary Algorithms for Rare Event Sampling', Available at: https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling.
Vancouver
Gideoni Y, Gal Y. Distribution Matching Evolutionary Algorithms for Rare Event Sampling. https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling
IEEE
Y. Gideoni, and Y. Gal, "Distribution Matching Evolutionary Algorithms for Rare Event Sampling," https://omanscience.com/en/articles/distribution-matching-evolutionary-algorithms-for-rare-event-sampling.