Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization

Gatopoulos, Ioannis; Lepert, Romain; Wiggers, Auke; Mariani, Giovanni; Tomczak, Jakub M.

doi:10.1109/cec48606.2020.9185646

Cited by 3 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, our results for applying surrogate models to the optimizers can be highly effective (i.e., speeding up convergence). It is a known fact (e.g., see [31,38]), nevertheless, we believe that the optimization with surrogate models has a great future and should be further investigated. For instance, considering other classes of surrogate models like Gaussian processes or (Bayesian) neural networks opens new opportunities and research questions worth following.…”

Section: Discussionmentioning

confidence: 95%

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

et al. 2021

View full text Add to dashboard Cite

One of the central elements in systems biology is the interaction between mathematical modeling and measured quantities. Typically, biological phenomena are represented as dynamical systems, and they are further analyzed and comprehended by identifying model parameters using experimental data. However, all model parameters cannot be found by gradient-based optimization methods by fitting the model to the experimental data due to the non-differentiable character of the problem. Here, we present POPI4SB, a Python-based framework for population-based parameter identification of dynamic models in systems biology. The code is built on top of PySCeS that provides an engine to run dynamic simulations. The idea behind the methodology is to provide a set of derivative-free optimization methods that utilize a population of candidate solutions to find a better solution iteratively. Additionally, we propose two surrogate-assisted population-based methods, namely, a combination of a k-nearest-neighbor regressor with the Reversible Differential Evolution and the Evolution of Distribution Algorithm, that speeds up convergence. We present the optimization framework on the example of the well-studied glycolytic pathway in Saccharomyces cerevisiae.

show abstract

Section: Discussionmentioning

confidence: 95%

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

et al. 2021

View full text Add to dashboard Cite

show abstract

“…However, these models are usually applied to individuals of the same size. On the other hand, another model used mainly for single objective optimization is the 𝑘-Nearest Neighbor for regression [50,72,73], which allows comparing two unequal-sized vectors by adjusting the distance measure. Unlike the original kNN method, the 𝐾 𝑁 𝑁 𝑅 method returns the average class label values of the 𝑘 neighbors of an instance instead of the class label frequency.…”

Section: Surrogate Model Creationmentioning

confidence: 99%

A Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-breakpoints and a Multi-objective Evolutionary Algorithm

Márquez-Grajales,

Mezura-Montes,

Acosta-Mesa

et al. 2024

Preprint

View full text Add to dashboard Cite

The enhanced multi-objective symbolic discretization for time series (eMODiTS) method employs a flexible discretization scheme using different value cuts for each non-equal time interval, which requires a computational cost for evaluating each objective function. Therefore, surrogate models were implemented to minimize this disadvantage. Nevertheless, each solution found by eMODiTS is a different-sized vector, so the surrogate model must be able to handle data sets under this characteristic. Consequently, this work's contribution lies in analyzing the surrogate models' implementation on the time series discretization, where each potential scheme is a real-number different-sized vector. For this reason, the surrogate model proposed was k-nearest Neighbors for regression with Dynamic Time Warping as a distance measure. Results suggest our proposal finds a suitable approximation to the final eMODiTS solutions with a functions evaluation reduction rate between 15% and 95%. However, according to Pareto front performance measures, the proposal Pareto front is competitive compared to the eMODiTS Pareto front, reaching an average Generational Distance (GD) between 0.0447 and 0.0536. Moreover, the average Hypervolume Ratio (HVR) ranges between 0.334 and 0.3891. Finally, our proposal compared against SAX-based methods presents a similar behavior regarding classification tasks and statistical tests.

show abstract

Machine learning enhancing metaheuristics: a systematic review

Oliveira¹,

Britto²,

Gusmão³

2023

Soft Comput

View full text Add to dashboard Cite

Evolutionary Algorithm with Non-parametric Surrogate Model for Tensor Program optimization

Cited by 3 publications

References 25 publications

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

Population-Based Parameter Identification for Dynamical Models of Biological Networks with an Application to Saccharomyces cerevisiae

A Surrogate-Assisted Symbolic Time-Series Discretization Using Multi-breakpoints and a Multi-objective Evolutionary Algorithm

Machine learning enhancing metaheuristics: a systematic review

Contact Info

Product

Resources

About