Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model

• URL: http://arxiv.org/abs/2509.11016v1
• Date: Sun, 14 Sep 2025 00:14:40 GMT
• Title: Deep Reinforcement Learning-Assisted Component Auto-Configuration of Differential Evolution Algorithm for Constrained Optimization: A Foundation Model
• Authors: Xu Yang, Rui Wang, Kaiwen Li, Wenhua Li, Ling Wang,
• Abstract summary: We introduce a novel framework for automated component configuration in Differential Evolution (DE) algorithm to address constrained optimization problems (COPs)<n>SuperDE is capable of recommending optimal per-generation configurations for unseen problems in a zero-shot manner.<n> Experimental results demonstrate that SuperDE significantly outperforms existing state-of-the-art algorithms on benchmark test suites.
• Score: 12.957830806430733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite significant efforts to manually design high-performance evolutionary algorithms, their adaptability remains limited due to the dynamic and ever-evolving nature of real-world problems. The "no free lunch" theorem highlights that no single algorithm performs optimally across all problems. While online adaptation methods have been proposed, they often suffer from inefficiency, weak convergence, and limited generalization on constrained optimization problems (COPs). To address these challenges, we introduce a novel framework for automated component configuration in Differential Evolution (DE) algorithm to address COPs, powered by Deep Reinforcement Learning (DRL). Specifically, we propose SuperDE, a foundation model that dynamically configures DE's evolutionary components based on real-time evolution. Trained offline through meta-learning across a wide variety of COPs, SuperDE is capable of recommending optimal per-generation configurations for unseen problems in a zero-shot manner. Utilizing a Double Deep Q-Network (DDQN), SuperDE adapts its configuration strategies in response to the evolving population states during optimization. Experimental results demonstrate that SuperDE significantly outperforms existing state-of-the-art algorithms on benchmark test suites, achieving superior generalization and optimization performance.

Related papers

• AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization [61.535567824938205]
  We introduce AdaEvolve, a framework that reformulates LLM-driven evolution as a hierarchical adaptive optimization problem.<n>AdaEvolve consistently outperforms the open-ended baselines across 185 different open-ended optimization problems.
  arXiv  Detail & Related papers  (2026-02-23T18:45:31Z)
• Detect and Act: Automated Dynamic Optimizer through Meta-Black-Box Optimization [19.31451943915537]
  We propose a reinforcement learning-assisted approach to enable automated variation detection and self-adaption in evolutionary algorithms.<n>Our approach could generalize toward unseen DOPs with automated environment variation detection and self-adaption.
  arXiv  Detail & Related papers  (2026-01-30T04:28:27Z)
• Socio-cognitive agent-oriented evolutionary algorithm with trust-based optimization [70.49434432747293]
  Trust-Based Optimization (TBO) is a novel extension of the island model in evolutionary computation that replaces conventional periodic migrations with a flexible, agent-driven interaction mechanism based on trust or reputation.<n> Experimental results demonstrate that TBO generally outperforms the standard island model evolutionary algorithm across various optimization problems.
  arXiv  Detail & Related papers  (2025-10-29T01:59:26Z)
• Large Language Model Assisted Automated Algorithm Generation and Evolution via Meta-black-box optimization [9.184788298623062]
  AwesomeDE is proposed that leverages large language models (LLMs) as the strategy of meta-optimizer to generate update rules for constrained evolutionary algorithm without human intervention.<n>Key components, including prompt design and iterative refinement, are systematically analyzed to determine their impact on design quality.<n> Experimental results demonstrate that the proposed approach outperforms existing methods in terms of computational efficiency and solution accuracy.
  arXiv  Detail & Related papers  (2025-09-16T17:02:24Z)
• Graph-Supported Dynamic Algorithm Configuration for Multi-Objective Combinatorial Optimization [5.481047026874548]
  This paper presents a novel graph neural network (GNN) based DRL to configure multi-objective evolutionary algorithms.<n>We model the dynamic algorithm configuration as a Markov decision process, representing the convergence of solutions in the objective space by a graph.<n> Experiments on diverse MOCO challenges indicate that our method outperforms traditional and DRL-based algorithm configuration methods in terms of efficacy and adaptability.
  arXiv  Detail & Related papers  (2025-05-22T09:53:54Z)
• Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization [14.116216795259554]
  Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms.<n>We introduce a novel framework that employs reinforcement learning (RL) to automatically design DE for black-box optimization.<n>RL acts as an advanced meta-optimizer, generating a customized DE configuration.
  arXiv  Detail & Related papers  (2025-01-22T13:41:47Z)
• Analyzing and Enhancing the Backward-Pass Convergence of Unrolled Optimization [50.38518771642365]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which often lacks a closed form. This paper provides theoretical insights into the backward pass of unrolled optimization, showing that it is equivalent to the solution of a linear system by a particular iterative method. A system called Folded Optimization is proposed to construct more efficient backpropagation rules from unrolled solver implementations.
  arXiv  Detail & Related papers  (2023-12-28T23:15:18Z)
• Advancements in Optimization: Adaptive Differential Evolution with Diversification Strategy [0.0]
  The study employs single-objective optimization in a two-dimensional space and runs ADEDS on each of the benchmark functions with multiple iterations. ADEDS consistently outperforms standard DE for a variety of optimization challenges, including functions with numerous local optima, plate-shaped, valley-shaped, stretched-shaped, and noisy functions.
  arXiv  Detail & Related papers  (2023-10-02T10:05:41Z)
• Backpropagation of Unrolled Solvers with Folded Optimization [55.04219793298687]
  The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. One typical strategy is algorithm unrolling, which relies on automatic differentiation through the operations of an iterative solver. This paper provides theoretical insights into the backward pass of unrolled optimization, leading to a system for generating efficiently solvable analytical models of backpropagation.
  arXiv  Detail & Related papers  (2023-01-28T01:50:42Z)
• Accelerated Federated Learning with Decoupled Adaptive Optimization [53.230515878096426]
  federated learning (FL) framework enables clients to collaboratively learn a shared model while keeping privacy of training data on clients. Recently, many iterations efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs)
  arXiv  Detail & Related papers  (2022-07-14T22:46:43Z)
• Speeding up Computational Morphogenesis with Online Neural Synthetic Gradients [51.42959998304931]
  A wide range of modern science and engineering applications are formulated as optimization problems with a system of partial differential equations (PDEs) as constraints. These PDE-constrained optimization problems are typically solved in a standard discretize-then-optimize approach. We propose a general framework to speed up PDE-constrained optimization using online neural synthetic gradients (ONSG) with a novel two-scale optimization scheme.
  arXiv  Detail & Related papers  (2021-04-25T22:43:51Z)
• Optimization-Inspired Learning with Architecture Augmentations and Control Mechanisms for Low-Level Vision [74.9260745577362]
  This paper proposes a unified optimization-inspired learning framework to aggregate Generative, Discriminative, and Corrective (GDC) principles. We construct three propagative modules to effectively solve the optimization models with flexible combinations. Experiments across varied low-level vision tasks validate the efficacy and adaptability of GDC.
  arXiv  Detail & Related papers  (2020-12-10T03:24:53Z)
• 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.