A Novel Population Initialization Method via Adaptive Experience Transfer for General-Purpose Binary Evolutionary Optimization

• URL: http://arxiv.org/abs/2512.00341v1
• Date: Sat, 29 Nov 2025 06:07:33 GMT
• Title: A Novel Population Initialization Method via Adaptive Experience Transfer for General-Purpose Binary Evolutionary Optimization
• Authors: Zhiyuan Wang, Shengcai Liu, Shaofeng Zhang, Ke Tang,
• Abstract summary: Evolutionary algorithms (EAs) are widely used general-purpose optimization methods.<n>However, under a limited number of function evaluations, the performance of EAs is quite sensitive to the quality of the initial population.
• Score: 23.8937164783691
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Evolutionary Algorithms (EAs) are widely used general-purpose optimization methods due to their domain independence. However, under a limited number of function evaluations (#FEs), the performance of EAs is quite sensitive to the quality of the initial population. Obtaining a high-quality initial population without problem-specific knowledge remains a significant challenge. To address this, this work proposes a general-purpose population initialization method, named mixture-of-experience for population initialization (MPI), for binary optimization problems where decision variables take values of 0 or 1. MPI leverages solving experiences from previously solved problems to generate high-quality initial populations for new problems using only a small number of FEs. Its main novelty lies in a general-purpose approach for representing, selecting, and transferring solving experiences without requiring problem-specific knowledge. Extensive experiments are conducted across six binary optimization problem classes, comprising three classic classes and three complex classes from real-world applications. The experience repository is constructed solely based on instances from the three classic classes, while the performance evaluation is performed across all six classes. The results demonstrate that MPI effectively transfers solving experiences to unseen problem classes (i.e., the complex ones) and higher-dimensional problem instances, significantly outperforming existing general-purpose population initialization methods.

Related papers

• Neural Predictor-Corrector: Solving Homotopy Problems with Reinforcement Learning [38.623998031868595]
  Homotopy paradigm appears across diverse domains such as robust optimization, global optimization, root-finding, and sampling.<n>We propose Neural Predictor-Corrector (NPC), which replaces hand-crafted NPC with automatically learned policies.<n>NPC consistently outperforms classical and specialized baselines in efficiency while demonstrating superior stability across tasks.
  arXiv  Detail & Related papers  (2026-02-03T04:19:48Z)
• Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs [102.48588475875749]
  We introduce Generative Self-Refinement (GSR), a novel parallel test-time scaling framework.<n>GSR generates a set of candidate responses in parallel and then performs self-refinement to synthesize a new superior solution.<n>We show that our method achieves state-of-the-art performance across five mathematical benchmarks.
  arXiv  Detail & Related papers  (2025-08-27T06:51:48Z)
• OMEGA: Can LLMs Reason Outside the Box in Math? Evaluating Exploratory, Compositional, and Transformative Generalization [88.76091817642963]
  Recent large-scale language models (LLMs) with long Chain-of-such reasoning as DeepSeek-R1-have achieved impressive results on Olympiad-level mathematics.<n>We introduce OMEGA-Out-of-distribution Math Problems Evaluation with 3 Generalization Axes-a benchmark designed to evaluate three axes of out-of-distribution generalization.
  arXiv  Detail & Related papers  (2025-06-23T17:51:40Z)
• A Knapsack by Any Other Name: Presentation impacts LLM performance on NP-hard problems [64.05451567422342]
  We introduce the dataset of Everyday Hard Optimization Problems (EHOP), a collection of NP-hard problems expressed in natural language.<n>EHOP includes problem formulations that could be found in computer science textbooks (e.g., graph coloring), versions that are dressed up as problems that could arise in real life.<n>We find that state-of-the-art LLMs, across multiple prompting strategies, solve textbook problems more accurately than their real-life and inverted counterparts.
  arXiv  Detail & Related papers  (2025-02-19T14:39:59Z)
• Evolving Generalizable Parallel Algorithm Portfolios via Domain-Agnostic Instance Generation [27.138566940625385]
  Generalization is the core objective when trainings from data.<n>Co-evolutionary approaches address this challenge by simultaneously evolving a parallel algorithm portfolio (PAP) and an instance population.<n>This work proposes a general-purpose, off-the-shelf PAP construction approach, named domain-agnostic co-evolution of parameterized search (DACE)
  arXiv  Detail & Related papers  (2025-01-06T10:29:48Z)
• Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review [43.27239522837257]
  This paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through transfer and inverse reinforcement learning (T-IRL)<n>Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies.<n>Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years.
  arXiv  Detail & Related papers  (2024-11-15T15:18:57Z)
• Learning Multiple Initial Solutions to Optimization Problems [52.9380464408756]
  Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications.<n>We propose learning to predict emphmultiple diverse initial solutions given parameters that define the problem instance.<n>We find significant and consistent improvement with our method across all evaluation settings and demonstrate that it efficiently scales with the number of initial solutions required.
  arXiv  Detail & Related papers  (2024-11-04T15:17:19Z)
• Learning Mixture-of-Experts for General-Purpose Black-Box Discrete Optimization [45.243090644194695]
  This article introduces MEGO, a novel general-purpose neural trained through a fully data-driven learning-to-optimize (L2O) approach. MEGO consists of a mixture-of-experts trained on experiences from solving training problems. MEGO actively selects relevant expert models to generate high-quality solutions.
  arXiv  Detail & Related papers  (2024-05-29T08:41:08Z)
• Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training [26.23887572937711]
  Multi-objective optimization problems (MOPs) require the simultaneous optimization of conflicting objectives.<n>We propose a Population Pre-trained Model (PPM) that leverages historical optimization knowledge to solve complex MOPs efficiently.<n>Our approach achieves robust generalization to downstream optimization tasks with up to 5,000 dimensions--five times the training scale and 200 times greater than prior work.
  arXiv  Detail & Related papers  (2023-12-11T05:16:58Z)
• Mining Large Independent Sets on Massive Graphs [47.21699378695116]
  ARCIS is an efficient algorithm for mining large independent sets on massive graphs.<n>We show that ARCIS attains the best or tied-best solution quality in most instances.
  arXiv  Detail & Related papers  (2022-08-16T14:39:38Z)
• Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions [78.31410227443102]
  We study the problem of adaptively identifying patient subpopulations that benefit from a given treatment during a confirmatory clinical trial. We propose AdaGGI and AdaGCPI, two meta-algorithms for subpopulation construction.
  arXiv  Detail & Related papers  (2022-08-11T14:27:49Z)
• Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack [26.91645793706187]
  Various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing. We propose an efficient local search algorithm (LS) to solve the problem in general cases. We show that LS can largely reduce the number of queries usually by an order of magnitude to achieve high attack success rates.
  arXiv  Detail & Related papers  (2021-09-06T03:44:43Z)
• Offline Model-Based Optimization via Normalized Maximum Likelihood Estimation [101.22379613810881]
  We consider data-driven optimization problems where one must maximize a function given only queries at a fixed set of points. This problem setting emerges in many domains where function evaluation is a complex and expensive process. We propose a tractable approximation that allows us to scale our method to high-capacity neural network models.
  arXiv  Detail & Related papers  (2021-02-16T06:04:27Z)

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.