Time efficiency in optimization with a bayesian-Evolutionary algorithm

Lan, Gongjin; Tomczak, Jakub M.; Roijers, Diederik M.; Eiben, A. E.

doi:10.1016/j.swevo.2021.100970

Cited by 39 publications

(20 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many approaches are interesting to further exploration. For instance, evolutionary approaches have been applied in many areas [3]. Neuroevolution have been applied in evolving neural network for real-time computer vision [4], evolutionary robotics [5,6,7,8].…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Pipeline for Hotel Recommendation System

Chen¹,

Gao²

2020

Preprint

View full text Add to dashboard Cite

This paper addresses a comprehensive pipeline to build a hotel recommendation system with the raw data collected by Apps in users' smartphone. The pipeline mainly consists of pre-process of the raw data and training prediction models. We use two methods, Support Vector Machine (SVM) and Recurrent Neural Network (RNN). The results show that two methods achieved a reasonable accuracy with the pre-process of the raw data. Therefore, we conclude that this paper provides a comprehensive pipeline, in which a hotel recommendation system was successful build from the raw data to specific applications.

show abstract

Section: Discussionmentioning

confidence: 99%

A Comprehensive Pipeline for Hotel Recommendation System

Chen¹,

Gao²

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Through the combination of these two contributions, the optimum region of a NiaH manifold can be quickly discovered in fewer experiments without pigeonholing into local minima. Thus, the three challenges of optimizing NiaH problems are addressed: (1) the challenge of finding a hypervolume within the manifold that contains the needle-like optimum [28,29,11], (2) the challenge of avoiding pigeonholing into local minima [9,1,30,31], (3) the challenge of the polynomially increasing compute times of BO using a GP surrogate [34,35,5,6,36,37]. We demonstrate the implementation of ZoMBI on a 5D analytical Ackley function, a 5D dataset of materials with Poisson's ratios, and a 5D dataset of thermoelectric materials, all of which exhibit a NiaH problem.…”

Section: Methodsmentioning

confidence: 99%

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization

Siemenn

Ren

et al. 2022

Preprint

View full text Add to dashboard Cite

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction, ecological resource management, fraud detection, and material property optimization. A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the dataset. For example, only 0.82% out of 146k total materials in the open-access Materials Project database have a negative Poisson's ratio. However, current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems, resulting in slow convergence to a global optimum or pigeonholing into a local minimum. In this paper, we present a Zooming Memory-Based Initialization algorithm, entitled ZoMBI, that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer experiments by addressing the common convergence and pigeonholing issues. ZoMBI actively extracts knowledge from the previously best-performing evaluated experiments to iteratively zoom in the sampling search bounds towards the global optimum "needle" and then prunes the memory of low-performing historical experiments to accelerate compute times by reducing the algorithm time complexity from O(n3) to O(1), as the number of experiments sampled increases. Additionally, ZoMBI implements two custom acquisition functions that use active learning to further guide the sampling of new experiments towards the global optimum. We validate the algorithm's performance on two real-world 5-dimensional Needle-in-a-Haystack material property optimization datasets: discovery of auxetic Poisson's ratio materials and discovery of high thermoelectric figure of merit materials. The ZoMBI algorithm demonstrates compute time speed-ups of 400x compared to traditional Bayesian optimization as well as efficiently discovering materials in under 100 experiments that are up to 3x more highly optimized than those discovered by current state-of-the-art algorithms.

show abstract

“…Regarding the controller learning algorithms, there are many recent papers applying learning algorithms to the brains of robots with fixed bodies in order to produce optimal brains ( Schembri et al, 2007 ; Ruud et al, 2017 ; Luck et al, 2019 ; Jelisavcic et al, 2019 ; Lan et al, 2020 ;; Schaff et al, 2019 ; Le Goff et al, 2020 ; van Diggelen et al, 2021 ; Nordmoen et al, 2021 ), naming only a few.…”

Section: Related Workmentioning

confidence: 99%

The Effects of Learning in Morphologically Evolving Robot Systems

et al. 2022

Self Cite

View full text Add to dashboard Cite

Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period has been proposed relatively long ago by the so-called Triangle of Life approach. However, an empirical assessment is still lacking to-date. In this paper, we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required to reach a certain fitness level compared to the purely evolutionary approach. Furthermore, we demonstrate that the evolved morphologies will be also different, even though learning only directly affects the controllers. This provides a quantitative demonstration that changes in the brain can induce changes in the body. Finally, we examine the learning delta defined as the performance difference between the inherited and the learned brain, and find that it is growing throughout the evolutionary process. This shows that evolution produces robots with an increasing plasticity, that is, consecutive generations become better learners and, consequently, they perform better at the given task. Moreover, our results demonstrate that the Triangle of Life is not only a concept of theoretical interest, but a system methodology with practical benefits.

show abstract

Time efficiency in optimization with a bayesian-Evolutionary algorithm

Cited by 39 publications

References 33 publications

A Comprehensive Pipeline for Hotel Recommendation System

A Comprehensive Pipeline for Hotel Recommendation System

Fast Bayesian Optimization of Needle-in-a-Haystack Problems using Zooming Memory-Based Initialization

The Effects of Learning in Morphologically Evolving Robot Systems

Contact Info

Product

Resources

About