Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation

• URL: http://arxiv.org/abs/2404.06557v1
• Date: Tue, 9 Apr 2024 18:20:16 GMT
• Title: Temporal True and Surrogate Fitness Landscape Analysis for Expensive Bi-Objective Optimisation
• Authors: C. J. Rodriguez, S. L. Thomson, T. Alderliesten, P. A. N. Bosman,
• Abstract summary: This study compares landscapes of the true fitness function with those of surrogate models for multi-objective functions. It does so temporally by examining landscape features at different points in time during optimisation. Despite differences, the true and surrogate landscape features still show high correlations between each other.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many real-world problems have expensive-to-compute fitness functions and are multi-objective in nature. Surrogate-assisted evolutionary algorithms are often used to tackle such problems. Despite this, literature about analysing the fitness landscapes induced by surrogate models is limited, and even non-existent for multi-objective problems. This study addresses this critical gap by comparing landscapes of the true fitness function with those of surrogate models for multi-objective functions. Moreover, it does so temporally by examining landscape features at different points in time during optimisation, in the vicinity of the population at that point in time. We consider the BBOB bi-objective benchmark functions in our experiments. The results of the fitness landscape analysis reveals significant differences between true and surrogate features at different time points during optimisation. Despite these differences, the true and surrogate landscape features still show high correlations between each other. Furthermore, this study identifies which landscape features are related to search and demonstrates that both surrogate and true landscape features are capable of predicting algorithm performance. These findings indicate that temporal analysis of the landscape features may help to facilitate the design of surrogate switching approaches to improve performance in multi-objective optimisation.

Related papers

• Task-adaptive Q-Face [75.15668556061772]
  We propose a novel task-adaptive multi-task face analysis method named as Q-Face. Q-Face simultaneously performs multiple face analysis tasks with a unified model. Our method achieves state-of-the-art performance on face expression recognition, action unit detection, face attribute analysis, age estimation, and face pose estimation.
  arXiv  Detail & Related papers  (2024-05-15T03:13:11Z)
• Deep-ELA: Deep Exploratory Landscape Analysis with Self-Supervised Pretrained Transformers for Single- and Multi-Objective Continuous Optimization Problems [0.5461938536945723]
  In this work, we propose a hybrid approach, Deep-ELA, which combines (the benefits of) deep learning and ELA features. Our proposed framework can either be used out-of-the-box for analyzing single- and multi-objective continuous optimization problems, or subsequently fine-tuned to various tasks.
  arXiv  Detail & Related papers  (2024-01-02T12:41:17Z)
• Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences [79.21515035128832]
  We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC) iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains.
  arXiv  Detail & Related papers  (2023-11-06T18:53:01Z)
• Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification [67.64124512185087]
  Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
  arXiv  Detail & Related papers  (2023-03-24T05:28:35Z)
• A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences [55.25331349436895]
  Deep generative models have emerged as a popular machine learning-based approach for inverse problems in the life sciences. These problems often require sampling new designs that satisfy multiple properties of interest in addition to learning the data distribution.
  arXiv  Detail & Related papers  (2022-10-19T19:04:45Z)
• A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes [0.0]
  Landscape insights are crucial for problem understanding as well as for assessing benchmark set diversity and composition. In this work we provide a collection of different approaches to characterize optimization landscapes. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning.
  arXiv  Detail & Related papers  (2022-04-12T12:46:31Z)
• Spatio-temporal Gait Feature with Adaptive Distance Alignment [90.5842782685509]
  We try to increase the difference of gait features of different subjects from two aspects: the optimization of network structure and the refinement of extracted gait features. Our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Adaptive Distance Alignment (ADA) ADA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted-temporal features to make them have low inter-class similarity and high intra-class similarity.
  arXiv  Detail & Related papers  (2022-03-07T13:34:00Z)
• Characterization of Constrained Continuous Multiobjective Optimization Problems: A Feature Space Perspective [0.0]
  constrained multiobjective optimization problems (CMOPs) are still unsatisfactory understood and characterized. We propose 29 landscape features (of which 19 are novel) to characterize CMOPs. We compare eight frequently used artificial test suites against a recently proposed suite consisting of real-world problems based on physical models.
  arXiv  Detail & Related papers  (2021-09-09T21:21:57Z)
• Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times [9.139734850798124]
  Multiobjetive evolutionary algorithms assume that each objective function can be evaluated within the same period of time. A co-surrogate is adopted to model the functional relationship between the fast and slow objective functions. A transferable instance selection method is introduced to acquire useful knowledge from the search process of the fast objective.
  arXiv  Detail & Related papers  (2021-08-30T16:10:15Z)
• Common Limitations of Image Processing Metrics: A Picture Story [58.83274952067888]
  This document focuses on biomedical image analysis problems that can be phrased as image-level classification, semantic segmentation, instance segmentation, or object detection task. The current version is based on a Delphi process on metrics conducted by an international consortium of image analysis experts from more than 60 institutions worldwide.
  arXiv  Detail & Related papers  (2021-04-12T17:03:42Z)
• Empirical Study on the Benefits of Multiobjectivization for Solving Single-Objective Problems [0.0]
  Local optima are often preventing algorithms from making progress and thus pose a severe threat. With the use of a sophisticated visualization technique based on the multi-objective gradients, the properties of the arising multi-objective landscapes are illustrated and examined. We will empirically show that the multi-objective COCO MOGSA is able to exploit these properties to overcome local traps.
  arXiv  Detail & Related papers  (2020-06-25T14:04:37Z)

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.