A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters

• URL: http://arxiv.org/abs/2504.21338v1
• Date: Wed, 30 Apr 2025 05:56:22 GMT
• Title: A Memetic Algorithm based on Variational Autoencoder for Black-Box Discrete Optimization with Epistasis among Parameters
• Authors: Aoi Kato, Kenta Kojima, Masahiro Nomura, Isao Ono,
• Abstract summary: Black-box discrete optimization (BB-DO) problems arise in many real-world applications.<n>Key challenge in BB-DO is epistasis among parameters where multiple variables must be modified simultaneously.<n>We propose a new memetic algorithm that combines VAE-based sampling with local search.
• Score: 2.6774008509841005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Black-box discrete optimization (BB-DO) problems arise in many real-world applications, such as neural architecture search and mathematical model estimation. A key challenge in BB-DO is epistasis among parameters where multiple variables must be modified simultaneously to effectively improve the objective function. Estimation of Distribution Algorithms (EDAs) provide a powerful framework for tackling BB-DO problems. In particular, an EDA leveraging a Variational Autoencoder (VAE) has demonstrated strong performance on relatively low-dimensional problems with epistasis while reducing computational cost. Meanwhile, evolutionary algorithms such as DSMGA-II and P3, which integrate bit-flip-based local search with linkage learning, have shown excellent performance on high-dimensional problems. In this study, we propose a new memetic algorithm that combines VAE-based sampling with local search. The proposed method inherits the strengths of both VAE-based EDAs and local search-based approaches: it effectively handles high-dimensional problems with epistasis among parameters without incurring excessive computational overhead. Experiments on NK landscapes -- a challenging benchmark for BB-DO involving epistasis among parameters -- demonstrate that our method outperforms state-of-the-art VAE-based EDA methods, as well as leading approaches such as P3 and DSMGA-II.

Related papers

• HyperArm Bandit Optimization: A Novel approach to Hyperparameter Optimization and an Analysis of Bandit Algorithms in Stochastic and Adversarial Settings [0.0]
  The paper introduces a novel framework, HyperArm Bandit Optimization (HABO), which applies EXP3 to hyper parameter tuning in machine learning models.<n>HABO treats individual hyper parameters as super-arms, and its potential configurations as sub-arms, enabling dynamic resource allocation and efficient exploration.
  arXiv  Detail & Related papers  (2025-03-13T11:50:28Z)
• Constrained Hybrid Metaheuristic Algorithm for Probabilistic Neural Networks Learning [0.3686808512438362]
  This study investigates the potential of hybrid metaheuristic algorithms to enhance the training of Probabilistic Neural Networks (PNNs)<n>Traditional learning methods, such as gradient-based approaches, often struggle to optimise high-dimensional and uncertain environments.<n>We propose the constrained Hybrid Metaheuristic (cHM) algorithm, which combines multiple population-based optimisation techniques into a unified framework.
  arXiv  Detail & Related papers  (2025-01-26T19:49:16Z)
• A Stochastic Approach to Bi-Level Optimization for Hyperparameter Optimization and Meta Learning [74.80956524812714]
  We tackle the general differentiable meta learning problem that is ubiquitous in modern deep learning. These problems are often formalized as Bi-Level optimizations (BLO) We introduce a novel perspective by turning a given BLO problem into a ii optimization, where the inner loss function becomes a smooth distribution, and the outer loss becomes an expected loss over the inner distribution.
  arXiv  Detail & Related papers  (2024-10-14T12:10:06Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• An Optimization-based Deep Equilibrium Model for Hyperspectral Image Deconvolution with Convergence Guarantees [71.57324258813675]
  We propose a novel methodology for addressing the hyperspectral image deconvolution problem. A new optimization problem is formulated, leveraging a learnable regularizer in the form of a neural network. The derived iterative solver is then expressed as a fixed-point calculation problem within the Deep Equilibrium framework.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem [0.0]
  This paper introduces a simulation of a well-known batch processing problem in the literature. Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) are used to find the optimal schedule for the simulation. We then compare the quality of solutions obtained by the surrogate-assisted versions of the algorithms against the baseline algorithms.
  arXiv  Detail & Related papers  (2022-10-31T09:00:39Z)
• Multi-objective hyperparameter optimization with performance uncertainty [62.997667081978825]
  This paper presents results on multi-objective hyperparameter optimization with uncertainty on the evaluation of Machine Learning algorithms. We combine the sampling strategy of Tree-structured Parzen Estimators (TPE) with the metamodel obtained after training a Gaussian Process Regression (GPR) with heterogeneous noise. Experimental results on three analytical test functions and three ML problems show the improvement over multi-objective TPE and GPR.
  arXiv  Detail & Related papers  (2022-09-09T14:58:43Z)
• Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces [54.58348769621782]
  Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.
  arXiv  Detail & Related papers  (2022-07-02T16:59:37Z)
• A Convergent ADMM Framework for Efficient Neural Network Training [17.764095204676973]
  Alternating Direction Method of Multipliers (ADMM) has achieved tremendous success in many classification and regression applications. We propose a novel framework to solve a general neural network training problem via ADMM (dlADMM) to address these challenges simultaneously. Experiments on seven benchmark datasets demonstrate the convergence, efficiency, and effectiveness of our proposed dlADMM algorithm.
  arXiv  Detail & Related papers  (2021-12-22T01:55:24Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• An Asymptotically Optimal Multi-Armed Bandit Algorithm and Hyperparameter Optimization [48.5614138038673]
  We propose an efficient and robust bandit-based algorithm called Sub-Sampling (SS) in the scenario of hyper parameter search evaluation. We also develop a novel hyper parameter optimization algorithm called BOSS. Empirical studies validate our theoretical arguments of SS and demonstrate the superior performance of BOSS on a number of applications.
  arXiv  Detail & Related papers  (2020-07-11T03:15:21Z)
• Objective-Sensitive Principal Component Analysis for High-Dimensional Inverse Problems [0.0]
  We present a novel approach for adaptive, differentiable parameterization of large-scale random fields. The developed technique is based on principal component analysis (PCA) but modifies a purely data-driven basis of principal components considering objective function behavior. Three algorithms for optimal parameter decomposition are presented and applied to an objective of 2D synthetic history matching.
  arXiv  Detail & Related papers  (2020-06-02T18:51:17Z)

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.