Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance

• URL: http://arxiv.org/abs/2507.01638v1
• Date: Wed, 02 Jul 2025 12:11:41 GMT
• Title: Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance
• Authors: Ana Nikolikj, Gabriela Ochoa, Tome Eftimov,
• Abstract summary: We present an analysis of landscape features for predicting the performance of multi-objective optimization algorithms.<n> benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation.<n>Our tailored analysis reveals feature combinations that influence algorithm performance specific to certain landscapes.
• Score: 2.9845592719739127
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We present an analysis of landscape features for predicting the performance of multi-objective combinatorial optimization algorithms. We consider features from the recently proposed compressed Pareto Local Optimal Solutions Networks (C-PLOS-net) model of combinatorial landscapes. The benchmark instances are a set of rmnk-landscapes with 2 and 3 objectives and various levels of ruggedness and objective correlation. We consider the performance of three algorithms -- Pareto Local Search (PLS), Global Simple EMO Optimizer (GSEMO), and Non-dominated Sorting Genetic Algorithm (NSGA-II) - using the resolution and hypervolume metrics. Our tailored analysis reveals feature combinations that influence algorithm performance specific to certain landscapes. This study provides deeper insights into feature importance, tailored to specific rmnk-landscapes and algorithms.

Related papers

• Meta-Learning Objectives for Preference Optimization [39.15940594751445]
  We show that it is possible to gain insights on the efficacy of preference optimization algorithms on simpler benchmarks.<n>We propose a novel family of PO algorithms based on mirror descent, which we call Mirror Preference Optimization (MPO)
  arXiv  Detail & Related papers  (2024-11-10T19:11:48Z)
• Multi-objective Memetic Algorithm with Adaptive Weights for Inverse Antenna Design [0.0]
  A memetic algorithm combining a gradient-based search for local minima with optimization is presented.<n>An important advancement is the adaptive weighting of objective functions during optimization.<n>The implemented algorithm applies to antenna inverse design problems and is an efficient data miner for machine learning tools.
  arXiv  Detail & Related papers  (2024-08-07T08:43:38Z)
• Hybridizing Target- and SHAP-encoded Features for Algorithm Selection in Mixed-variable Black-box Optimization [0.9049664874474738]
  ELA is used for problem comprehension, algorithm design, and applications such as automated algorithm selection and configuration. Until recently, ELA was limited to search spaces with either continuous or discrete variables, neglecting problems with mixed variable types. This gap was addressed in a recent study that uses an approach based on target-encoding to compute exploratory landscape features for mixed problems.
  arXiv  Detail & Related papers  (2024-07-10T07:47:31Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search [104.45426861115972]
  We propose to directly generate structural parameters by utilizing the specifically designed hyper kernels. We obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. A series of experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-04-23T17:27:40Z)
• An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization [78.36413169647408]
  We study the effectiveness of various ZO optimization methods for optimizing molecular objectives. We show the advantages of ZO sign-based gradient descent (ZO-signGD) We demonstrate the potential effectiveness of ZO optimization methods on widely used benchmark tasks from the Guacamol suite.
  arXiv  Detail & Related papers  (2022-10-27T01:58:10Z)
• 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)
• Explainable Landscape-Aware Optimization Performance Prediction [0.0]
  We are investigating explainable landscape-aware regression models. The contribution of each landscape feature to the prediction of the optimization algorithm performance is estimated on a global and local level. The results show a proof of concept that different set of features are important for different problem instances.
  arXiv  Detail & Related papers  (2021-10-22T07:46:33Z)
• Generalization in portfolio-based algorithm selection [97.74604695303285]
  We provide the first provable guarantees for portfolio-based algorithm selection. We show that if the portfolio is large, overfitting is inevitable, even with an extremely simple algorithm selector.
  arXiv  Detail & Related papers  (2020-12-24T16:33:17Z)
• Multi-layer local optima networks for the analysis of advanced local search-based algorithms [0.6299766708197881]
  Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular optimization problem based on a specific neighborhood operator and a local search algorithm. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis.
  arXiv  Detail & Related papers  (2020-04-29T03:20:01Z)
• Mixed Strategies for Robust Optimization of Unknown Objectives [93.8672371143881]
  We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter. We design a novel sample-efficient algorithm GP-MRO, which sequentially learns about the unknown objective from noisy point evaluations. GP-MRO seeks to discover a robust and randomized mixed strategy, that maximizes the worst-case expected objective value.
  arXiv  Detail & Related papers  (2020-02-28T09:28:17Z)

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.