# Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm

arXiv: https://arxiv.org/abs/2005.04166 · HTML: https://mlsanity.com/paper/2005.04166/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 27 of 27)

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- [Real-time pedestrian recognition on low computational resources](https://mlsanity.com/paper/2309.01353/) (2023)
- [Automated Parking Planning with Vision-Based BEV Approach](https://mlsanity.com/paper/2406.15430/) (2024)
- [Acceleration method for generating perception failure scenarios based on editing Markov process](https://mlsanity.com/paper/2407.00980/) (2024)
- [Deep Reinforcement Learning for Adverse Garage Scenario Generation](https://mlsanity.com/paper/2407.01333/) (2024)
- [Research on Reliable and Safe Occupancy Grid Prediction in Underground Parking Lots](https://mlsanity.com/paper/2407.02197/) (2024)
- [Development of Occupancy Prediction Algorithm for Underground Parking Lots](https://mlsanity.com/paper/2409.00923/) (2024)
- [Manboformer: Learning Gaussian Representations via Spatial-temporal Attention Mechanism](https://mlsanity.com/paper/2503.04863/) (2025)
- [Data Shift of Object Detection in Autonomous Driving](https://mlsanity.com/paper/2508.11868/) (2025)

## Abstract (arXiv)

Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. Evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both the BO and EA community typically assess their performance as a function of the number of evaluations. However, this is unfair once we start to compare the efficiency of these classes of algorithms, as the overhead times to generate candidate solutions are significantly different. We suggest to measure the efficiency of generate-and-test search algorithms as the expected gain in the objective value per unit of computation time spent. We observe that the preference of an algorithm to be used can change after a number of function evaluations. We therefore propose a new algorithm, a combination of Bayesian optimization and an Evolutionary Algorithm, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. We compare the BEA with BO and the EA. The results show that BEA outperforms both BO and the EA in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima. Moreover, we test the three algorithms on nine test cases of robot learning problems and here again we find that BEA outperforms the other algorithms.