Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)

• URL: http://arxiv.org/abs/2510.09391v1
• Date: Fri, 10 Oct 2025 13:45:32 GMT
• Title: Fast and robust parametric and functional learning with Hybrid Genetic Optimisation (HyGO)
• Authors: Isaac Robledo, Yiqing Li, Guy Y. Cornejo Maceda, Rodrigo Castellanos,
• Abstract summary: HyGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement.<n>For parametric problems, HyGO alternates between a genetic algorithm and targeted improvement through a degradation-proof Dowhill Simplex Method (DSM)<n>For function optimisation tasks, HyGO rotates between genetic programming and DSM.
• Score: 0.5628958216121255
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The Hybrid Genetic Optimisation framework (HyGO) is introduced to meet the pressing need for efficient and unified optimisation frameworks that support both parametric and functional learning in complex engineering problems. Evolutionary algorithms are widely employed as derivative-free global optimisation methods but often suffer from slow convergence rates, especially during late-stage learning. HyGO integrates the global exploration capabilities of evolutionary algorithms with accelerated local search for robust solution refinement. The key enabler is a two-stage strategy that balances exploration and exploitation. For parametric problems, HyGO alternates between a genetic algorithm and targeted improvement through a degradation-proof Dowhill Simplex Method (DSM). For function optimisation tasks, HyGO rotates between genetic programming and DSM. Validation is performed on (a) parametric optimisation benchmarks, where HyGO demonstrates faster and more robust convergence than standard genetic algorithms, and (b) function optimisation tasks, including control of a damped Landau oscillator. Practical relevance is showcased through aerodynamic drag reduction of an Ahmed body via Reynolds-Averaged Navier-Stokes simulations, achieving consistently interpretable results and reductions exceeding 20% by controlled jet injection in the back of the body for flow reattachment and separation bubble reduction. Overall, HyGO emerges as a versatile hybrid optimisation framework suitable for a broad spectrum of engineering and scientific problems involving parametric and functional learning.

Related papers

• Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments [0.44654729785785796]
  We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to simplify the search problem.<n>We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system.<n>Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning.
  arXiv  Detail & Related papers  (2026-02-11T09:15:20Z)
• GEGO: A Hybrid Golden Eagle and Genetic Optimization Algorithm for Efficient Hyperparameter Tuning in Resource-Constrained Environments [4.417564179511245]
  HyperEGO tuning is a critical yet computationally expensive step in hyper search networks.<n>Golden Eagle Optimization (GEGO) integrates a hybrid movement strategy of Golden Eagle operators of selection and mutation.<n>GEGO is evaluated on standard unimodal, multimodal, and composite benchmark functions.
  arXiv  Detail & Related papers  (2026-01-21T05:35:38Z)
• Graph Neural Network Assisted Genetic Algorithm for Structural Dynamic Response and Parameter Optimization [1.5383027029023142]
  optimization of structural parameters, such as mass(m), stiffness(k), and damping coefficient(c) is critical for designing efficient, resilient, and stable structures.<n>This study proposes a hybrid data-driven framework that integrates a Graph Neural Network (GNN) surrogate model with a Genetic Algorithm (GA) to overcome these challenges.
  arXiv  Detail & Related papers  (2025-10-26T21:14:59Z)
• Optimising 4th-Order Runge-Kutta Methods: A Dynamic Heuristic Approach for Efficiency and Low Storage [0.0]
  Genetic Algorithm (GA) and Reinforcement Learning (RL) approach for automated discovery, optimising low-storage Runge-Kutta methods.<n>GA-driven mutations for search-space exploration and an RL-inspired state transition mechanism to refine selection dynamically.<n>Best-performing Genetic Algorithm achieves a 25% reduction in IPOPT compared to traditional ESRK optimisation processes.
  arXiv  Detail & Related papers  (2025-06-26T16:51:22Z)
• Synergizing Reinforcement Learning and Genetic Algorithms for Neural Combinatorial Optimization [25.633698252033756]
  We propose the Evolutionary Augmentation Mechanism (EAM) to synergize the learning efficiency of DRL with the global search power of GAs.<n>EAM operates by generating solutions from a learned policy and refining them through domain-specific genetic operations such as crossover and mutation.<n>EAM can be seamlessly integrated with state-of-the-art DRL solvers such as the Attention Model, POMO, and SymNCO.
  arXiv  Detail & Related papers  (2025-06-11T05:17:30Z)
• Goat Optimization Algorithm: A Novel Bio-Inspired Metaheuristic for Global Optimization [1.2289361708127877]
  This paper presents a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.<n>The algorithm's performance is evaluated on standard unimodal benchmark functions.<n>The findings suggest that GOA is a promising advancement in bio-inspired optimization techniques.
  arXiv  Detail & Related papers  (2025-03-04T06:44:07Z)
• 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)
• Improved Genetic Algorithm Based on Greedy and Simulated Annealing Ideas for Vascular Robot Ordering Strategy [7.51372615162241]
  This study presents a comprehensive approach for optimizing the acquisition, utilization, and maintenance of ABLVR vascular robots in healthcare settings. Considering the dynamic healthcare environment, our approach includes a robust resource allocation model for robotic vessels and operators. We incorporate the unique requirements of the adaptive learning process for operators and the maintenance needs of robotic components.
  arXiv  Detail & Related papers  (2024-03-28T15:14:03Z)
• Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations [50.591267188664666]
  Propulate is an evolutionary optimization algorithm and software package for global optimization. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy.
  arXiv  Detail & Related papers  (2023-01-20T18:17:34Z)
• Nesterov Meets Optimism: Rate-Optimal Separable Minimax Optimization [108.35402316802765]
  We propose a new first-order optimization algorithm -- AcceleratedGradient-OptimisticGradient (AG-OG) Ascent. We show that AG-OG achieves the optimal convergence rate (up to a constant) for a variety of settings. We extend our algorithm to extend the setting and achieve the optimal convergence rate in both bi-SC-SC and bi-C-SC settings.
  arXiv  Detail & Related papers  (2022-10-31T17:59:29Z)
• 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)
• SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients [99.13839450032408]
  It is desired to design a universal framework for adaptive algorithms to solve general problems. In particular, our novel framework provides adaptive methods under non convergence support for setting.
  arXiv  Detail & Related papers  (2021-06-15T15:16:28Z)
• Optimizing Large-Scale Hyperparameters via Automated Learning Algorithm [97.66038345864095]
  We propose a new hyperparameter optimization method with zeroth-order hyper-gradients (HOZOG) Specifically, we first formulate hyperparameter optimization as an A-based constrained optimization problem. Then, we use the average zeroth-order hyper-gradients to update hyper parameters.
  arXiv  Detail & Related papers  (2021-02-17T21:03:05Z)
• EOS: a Parallel, Self-Adaptive, Multi-Population Evolutionary Algorithm for Constrained Global Optimization [68.8204255655161]
  EOS is a global optimization algorithm for constrained and unconstrained problems of real-valued variables. It implements a number of improvements to the well-known Differential Evolution (DE) algorithm. Results prove that EOSis capable of achieving increased performance compared to state-of-the-art single-population self-adaptive DE algorithms.
  arXiv  Detail & Related papers  (2020-07-09T10:19:22Z)

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.