GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization

• URL: http://arxiv.org/abs/2312.07034v1
• Date: Tue, 12 Dec 2023 07:40:12 GMT
• Title: GNBG-Generated Test Suite for Box-Constrained Numerical Global Optimization
• Authors: Amir H. Gandomi (1,2), Danial Yazdani (1), Mohammad Nabi Omidvar (3), and Kalyanmoy Deb (4) ((1) Faculty of Engineering & Information Technology, University of Technology Sydney, (2) University Research and Innovation Center (EKIK), Obuda University, (3) School of Computing, University of Leeds, and Leeds University Business School, (4) BEACON Center, Michigan State University)
• Abstract summary: This document introduces a set of 24 box-constrained numerical global optimization problem instances. Cases cover a broad spectrum of problem features, including varying degrees of modality, ruggedness, symmetry, conditioning, variable interaction structures, basin linearity, and deceptiveness.
• Score: 5.804807909435654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This document introduces a set of 24 box-constrained numerical global optimization problem instances, systematically constructed using the Generalized Numerical Benchmark Generator (GNBG). These instances cover a broad spectrum of problem features, including varying degrees of modality, ruggedness, symmetry, conditioning, variable interaction structures, basin linearity, and deceptiveness. Purposefully designed, this test suite offers varying difficulty levels and problem characteristics, facilitating rigorous evaluation and comparative analysis of optimization algorithms. By presenting these problems, we aim to provide researchers with a structured platform to assess the strengths and weaknesses of their algorithms against challenges with known, controlled characteristics. For reproducibility, the MATLAB source code for this test suite is publicly available.

Related papers

• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• Absolute Ranking: An Essential Normalization for Benchmarking Optimization Algorithms [0.0]
  evaluating performance across optimization algorithms on many problems presents a complex challenge due to the diversity of numerical scales involved. This paper extensively explores the problem, making a compelling case to underscore the issue and conducting a thorough analysis of its root causes. Building on this research, this paper introduces a new mathematical model called "absolute ranking" and a sampling-based computational method.
  arXiv  Detail & Related papers  (2024-09-06T00:55:03Z)
• A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization [12.016449555335976]
  We propose an open-source benchmark suite MaxCut-Bench dedicated to the NP-hard Maximum Cut problem in both its weighted and unweighted variants. We use the benchmark in an attempt to systematically corroborate or reproduce the results of several, popular learning-based approaches. Our results show that several of the learneds fail to outperform a naive greedy algorithm, and that only one of them consistently outperforms Tabu Search.
  arXiv  Detail & Related papers  (2024-06-14T19:44:23Z)
• GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization [5.635586285644365]
  It is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization.
  arXiv  Detail & Related papers  (2023-12-12T09:04:34Z)
• Amortized Implicit Differentiation for Stochastic Bilevel Optimization [53.12363770169761]
  We study a class of algorithms for solving bilevel optimization problems in both deterministic and deterministic settings. We exploit a warm-start strategy to amortize the estimation of the exact gradient. By using this framework, our analysis shows these algorithms to match the computational complexity of methods that have access to an unbiased estimate of the gradient.
  arXiv  Detail & Related papers  (2021-11-29T15:10:09Z)
• Generating Large-scale Dynamic Optimization Problem Instances Using the Generalized Moving Peaks Benchmark [9.109331015600185]
  This document describes the generalized moving peaks benchmark (GMPB) and how it can be used to generate problem instances for continuous large-scale dynamic optimization problems. It presents a set of 15 benchmark problems, the relevant source code, and a performance indicator, designed for comparative studies and competitions in large-scale dynamic optimization.
  arXiv  Detail & Related papers  (2021-07-23T03:57:50Z)
• Harnessing Heterogeneity: Learning from Decomposed Feedback in Bayesian Modeling [68.69431580852535]
  We introduce a novel GP regression to incorporate the subgroup feedback. Our modified regression has provably lower variance -- and thus a more accurate posterior -- compared to previous approaches. We execute our algorithm on two disparate social problems.
  arXiv  Detail & Related papers  (2021-07-07T03:57:22Z)
• Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms [71.62575565990502]
  We prove that the generalization error of an optimization algorithm can be bounded on the complexity' of the fractal structure that underlies its generalization measure. We further specialize our results to specific problems (e.g., linear/logistic regression, one hidden/layered neural networks) and algorithms.
  arXiv  Detail & Related papers  (2021-06-09T08:05:36Z)
• Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization [98.0595480384208]
  We propose a generalization extraient spaces which converges to a stationary point. The algorithm applies not only to general $p$-normed spaces, but also to general $p$-dimensional vector spaces.
  arXiv  Detail & Related papers  (2020-10-31T21:35:42Z)
• Total Deep Variation: A Stable Regularizer for Inverse Problems [71.90933869570914]
  We introduce the data-driven general-purpose total deep variation regularizer. In its core, a convolutional neural network extracts local features on multiple scales and in successive blocks. We achieve state-of-the-art results for numerous imaging tasks.
  arXiv  Detail & Related papers  (2020-06-15T21:54:15Z)
• Scalable and Customizable Benchmark Problems for Many-Objective Optimization [0.0]
  We propose a parameterized generator of scalable and customizable benchmark problems for many-objective problems (MaOPs) It is able to generate problems that reproduce features present in other benchmarks and also problems with some new features.
  arXiv  Detail & Related papers  (2020-01-26T12:39:51Z)

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.