Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization

• URL: http://arxiv.org/abs/2309.11994v2
• Date: Sun, 8 Oct 2023 09:04:34 GMT
• Title: Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
• Authors: Hao Hao and Xiaoqun Zhang and Aimin Zhou
• Abstract summary: This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation.
• Score: 6.382398222493027
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) hold significant importance in resolving expensive optimization problems~(EOPs). Extensive efforts have been devoted to improving the efficacy of SAEAs through the development of proficient model-assisted selection methods. However, generating high-quality solutions is a prerequisite for selection. The fundamental paradigm of evaluating a limited number of solutions in each generation within SAEAs reduces the variance of adjacent populations, thus impacting the quality of offspring solutions. This is a frequently encountered issue, yet it has not gained widespread attention. This paper presents a framework using unevaluated solutions to enhance the efficiency of SAEAs. The surrogate model is employed to identify high-quality solutions for direct generation of new solutions without evaluation. To ensure dependable selection, we have introduced two tailored relation models for the selection of the optimal solution and the unevaluated population. A comprehensive experimental analysis is performed on two test suites, which showcases the superiority of the relation model over regression and classification models in the selection phase. Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.

Related papers

• Un-evaluated Solutions May Be Valuable in Expensive Optimization [5.6787965501364335]
  We propose a strategic approach that incorporates high-quality, un-evaluated solutions predicted by surrogate models during the selection phase. This approach aims to improve the distribution of evaluated solutions, thereby generating a superior next generation of solutions.
  arXiv  Detail & Related papers  (2024-12-05T04:06:30Z)
• Learning Joint Models of Prediction and Optimization [56.04498536842065]
  Predict-Then-Then framework uses machine learning models to predict unknown parameters of an optimization problem from features before solving. This paper proposes an alternative method, in which optimal solutions are learned directly from the observable features by joint predictive models.
  arXiv  Detail & Related papers  (2024-09-07T19:52:14Z)
• A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms [5.198324938447394]
  Surrogate-assisted Evolutionary Algorithm (SAEA) is an essential method for solving expensive problems. This paper introduces Kolmogorov-Arnold Networks (KANs) as surrogate models within SAEAs. KANs demonstrate commendable performance within SAEAs, effectively decreasing the number of function calls and enhancing the optimization efficiency.
  arXiv  Detail & Related papers  (2024-05-26T09:12:44Z)
• An Efficient Approach for Solving Expensive Constrained Multiobjective Optimization Problems [0.0]
  An efficient probabilistic selection based constrained multi-objective EA is proposed, referred to as PSCMOEA. It comprises novel elements such as (a) an adaptive search bound identification scheme based on the feasibility and convergence status of evaluated solutions. Numerical experiments are conducted on an extensive range of challenging constrained problems using low evaluation budgets to simulate ECMOPs.
  arXiv  Detail & Related papers  (2024-05-22T02:32:58Z)
• Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis [5.6787965501364335]
  Black-box optimization problems are common in many real-world applications. These problems require optimization through input-output interactions without access to internal workings. Two widely used gradient-free optimization techniques are employed to address such challenges. This paper aims to elucidate the similarities and differences in the utilization of model uncertainty between these two methods.
  arXiv  Detail & Related papers  (2024-03-21T13:59:19Z)
• V-STaR: Training Verifiers for Self-Taught Reasoners [71.53113558733227]
  V-STaR trains a verifier using DPO that judges correctness of model-generated solutions. Running V-STaR for multiple iterations results in progressively better reasoners and verifiers.
  arXiv  Detail & Related papers  (2024-02-09T15:02:56Z)
• Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraints [51.12047280149546]
  A direct approach for obtaining a fair predictive model is to train the model through optimizing its prediction performance subject to fairness constraints. We formulate the training problem of a fairness-aware machine learning model as an AUC optimization problem subject to a class of AUC-based fairness constraints. We demonstrate the effectiveness of our approach on real-world data under different fairness metrics.
  arXiv  Detail & Related papers  (2022-12-23T22:29:08Z)
• Learning Proximal Operators to Discover Multiple Optima [66.98045013486794]
  We present an end-to-end method to learn the proximal operator across non-family problems. We show that for weakly-ized objectives and under mild conditions, the method converges globally.
  arXiv  Detail & Related papers  (2022-01-28T05:53:28Z)
• Modeling the Second Player in Distributionally Robust Optimization [90.25995710696425]
  We argue for the use of neural generative models to characterize the worst-case distribution. This approach poses a number of implementation and optimization challenges. We find that the proposed approach yields models that are more robust than comparable baselines.
  arXiv  Detail & Related papers  (2021-03-18T14:26:26Z)
• Automatically Learning Compact Quality-aware Surrogates for Optimization Problems [55.94450542785096]
  Solving optimization problems with unknown parameters requires learning a predictive model to predict the values of the unknown parameters and then solving the problem using these values. Recent work has shown that including the optimization problem as a layer in a complex training model pipeline results in predictions of iteration of unobserved decision making. We show that we can improve solution quality by learning a low-dimensional surrogate model of a large optimization problem.
  arXiv  Detail & Related papers  (2020-06-18T19:11:54Z)
• Surrogate Assisted Evolutionary Algorithm for Medium Scale Expensive Multi-Objective Optimisation Problems [4.338938227238059]
  Building a surrogate model of an objective function has shown to be effective to assist evolutionary algorithms (EAs) to solve real-world complex optimisation problems. We propose a Gaussian process surrogate model assisted EA for medium-scale expensive multi-objective optimisation problems with up to 50 decision variables. The effectiveness of our proposed algorithm is validated on benchmark problems with 10, 20, 50 variables, comparing with three state-of-the-art SAEAs.
  arXiv  Detail & Related papers  (2020-02-08T12:06:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.