Using coevolution and substitution of the fittest for health and well-being recommender systems

• URL: http://arxiv.org/abs/2211.00414v1
• Date: Tue, 1 Nov 2022 12:16:11 GMT
• Title: Using coevolution and substitution of the fittest for health and well-being recommender systems
• Authors: Hugo Alcaraz-Herrera and John Cartlidge
• Abstract summary: substitution of the fittest (SF) is a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. We then address the more complex real-world problem of evolving recommendations for health and well-being.
• Score: 3.8073142980733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.

Related papers

• Learning Evolution via Optimization Knowledge Adaptation [50.280704114978384]
  Evolutionary algorithms (EAs) maintain populations through evolutionary operators to discover solutions for complex tasks. We introduce an Optimization Knowledge Adaptation Evolutionary Model (OKAEM) to enhance its optimization capabilities. OKAEM exploits prior knowledge for significant performance gains across various knowledge transfer settings. It is capable of emulating principles of natural selection and genetic recombination.
  arXiv  Detail & Related papers  (2025-01-04T05:35:21Z)
• Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation [52.36436121884317]
  We show that Source-Free Domain Adaptation (SFDA) generally outperforms Unsupervised Domain Adaptation (UDA) in real-world scenarios. SFDA offers advantages in time efficiency, storage requirements, targeted learning objectives, reduced risk of negative transfer, and increased robustness against overfitting. We propose a novel weight estimation method that effectively integrates available source data into multi-SFDA approaches.
  arXiv  Detail & Related papers  (2024-11-24T13:49:29Z)
• An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems [5.095287502726488]
  We propose a strategy recombination and reconstruction differential evolution algorithm called reconstructed differential evolution (RDE) to solve single-objective bounded optimization problems. Based on the benchmark suite of the 2024 IEEE Congress on Evolutionary Computation, we tested RDE and several other advanced differential evolution variants.
  arXiv  Detail & Related papers  (2024-04-25T01:48:44Z)
• Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems [2.1756081703276]
  We use two real-world computational fluid dynamics problems to compare the performance of eleven state-of-the-art single-objective SAEAs. Our findings suggest that the more recently published methods, as well as the techniques that utilize differential evolution as one of their optimization mechanisms, perform significantly better than the other considered methods.
  arXiv  Detail & Related papers  (2024-02-26T09:58:36Z)
• CoSDA: Continual Source-Free Domain Adaptation [78.47274343972904]
  Without access to the source data, source-free domain adaptation (SFDA) transfers knowledge from a source-domain trained model to target domains. Recently, SFDA has gained popularity due to the need to protect the data privacy of the source domain, but it suffers from catastrophic forgetting on the source domain due to the lack of data. We propose a continual source-free domain adaptation approach named CoSDA, which employs a dual-speed optimized teacher-student model pair and is equipped with consistency learning capability.
  arXiv  Detail & Related papers  (2023-04-13T15:53:23Z)
• In Search for a Generalizable Method for Source Free Domain Adaptation [9.032468541589203]
  Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFDA techniques to a challenging set of naturally-occurring distribution shifts in bioacoustics. We find existing methods perform differently relative to each other than observed in vision benchmarks, and sometimes perform worse than no adaptation at all.
  arXiv  Detail & Related papers  (2023-02-13T19:43:16Z)
• Effective Mutation Rate Adaptation through Group Elite Selection [50.88204196504888]
  This paper introduces the Group Elite Selection of Mutation Rates (GESMR) algorithm. GESMR co-evolves a population of solutions and a population of MRs, such that each MR is assigned to a group of solutions. With the same number of function evaluations and with almost no overhead, GESMR converges faster and to better solutions than previous approaches.
  arXiv  Detail & Related papers  (2022-04-11T01:08:26Z)
• Evolutionary Diversity Optimisation for The Traveling Thief Problem [11.590506672325668]
  We introduce a bi-level evolutionary algorithm to maximise the structural diversity of the set of solutions. We empirically determine the best method to obtain diversity. Our experimental results show a significant improvement of the QD approach in terms of structural diversity for most TTP benchmark instances.
  arXiv  Detail & Related papers  (2022-04-06T10:13:55Z)
• Substitution of the Fittest: A Novel Approach for Mitigating Disengagement in Coevolutionary Genetic Algorithms [3.8073142980733]
  substitution of the fittest (SF) designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. In a minimal domain, we perform a controlled evaluation of the ability to maintain engagement and the capacity to discover optimal solutions.
  arXiv  Detail & Related papers  (2021-08-06T15:10:36Z)
• Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction [61.62191904755521]
  3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. Existing deep learning based methods only focus on the basic cases of skill sequences. We propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
  arXiv  Detail & Related papers  (2021-07-31T16:06:50Z)
• 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)

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.