GLHF: General Learned Evolutionary Algorithm Via Hyper Functions

• URL: http://arxiv.org/abs/2405.03728v1
• Date: Mon, 6 May 2024 09:11:49 GMT
• Title: GLHF: General Learned Evolutionary Algorithm Via Hyper Functions
• Authors: Xiaobin Li, Kai Wu, Yujian Betterest Li, Xiaoyu Zhang, Handing Wang, Jing Liu,
• Abstract summary: General pre-trained optimization model (GPOM) outperforms state-of-the-art evolutionary algorithms and pretrained optimization models (POMs) GPOM exhibits robust generalization capabilities across diverse task distributions, dimensions, population sizes, and optimization horizons.
• Score: 16.391389860521134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pretrained Optimization Models (POMs) leverage knowledge gained from optimizing various tasks, providing efficient solutions for new optimization challenges through direct usage or fine-tuning. Despite the inefficiencies and limited generalization abilities observed in current POMs, our proposed model, the general pre-trained optimization model (GPOM), addresses these shortcomings. GPOM constructs a population-based pretrained Black-Box Optimization (BBO) model tailored for continuous optimization. Evaluation on the BBOB benchmark and two robot control tasks demonstrates that GPOM outperforms other pretrained BBO models significantly, especially for high-dimensional tasks. Its direct optimization performance exceeds that of state-of-the-art evolutionary algorithms and POMs. Furthermore, GPOM exhibits robust generalization capabilities across diverse task distributions, dimensions, population sizes, and optimization horizons.

Related papers

• High-Dimensional Bayesian Optimization Using Both Random and Supervised Embeddings [0.6291443816903801]
  This paper proposes a high-dimensionnal optimization method incorporating linear embedding subspaces of small dimension. The resulting BO method combines in an adaptive way both random and supervised linear embeddings. The obtained results show the high potential of EGORSE to solve high-dimensional blackbox optimization problems.
  arXiv  Detail & Related papers  (2025-02-02T16:57:05Z)
• Optima: Optimizing Effectiveness and Efficiency for LLM-Based Multi-Agent System [75.25394449773052]
  Large Language Model (LLM) based multi-agent systems (MAS) show remarkable potential in collaborative problem-solving. Yet they still face critical challenges: low communication efficiency, poor scalability, and a lack of effective parameter-updating optimization methods. We present Optima, a novel framework that addresses these issues by significantly enhancing both communication efficiency and task effectiveness.
  arXiv  Detail & Related papers  (2024-10-10T17:00:06Z)
• Localized Zeroth-Order Prompt Optimization [54.964765668688806]
  We propose a novel algorithm, namely localized zeroth-order prompt optimization (ZOPO) ZOPO incorporates a Neural Tangent Kernel-based derived Gaussian process into standard zeroth-order optimization for an efficient search of well-performing local optima in prompt optimization. Remarkably, ZOPO outperforms existing baselines in terms of both the optimization performance and the query efficiency.
  arXiv  Detail & Related papers  (2024-03-05T14:18:15Z)
• MADA: Meta-Adaptive Optimizers through hyper-gradient Descent [73.1383658672682]
  We introduce Meta-Adaptives (MADA), a unified framework that can generalize several known convergences and dynamically learn the most suitable one during training. We empirically compare MADA to other populars on vision and language tasks, and find that MADA consistently outperforms Adam and other populars. We also propose AVGrad, a modification of AMS that replaces the maximum operator with averaging, which is more suitable for hyper-gradient optimization.
  arXiv  Detail & Related papers  (2024-01-17T00:16:46Z)
• Large Language Models as Optimizers [106.52386531624532]
  We propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as prompts. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values. We demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.
  arXiv  Detail & Related papers  (2023-09-07T00:07:15Z)
• 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)
• Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview [10.081056751778712]
  We introduce the basics of multi-objective hyperparameter optimization and motivate its usefulness in applied ML. We provide an extensive survey of existing optimization strategies, both from the domain of evolutionary algorithms and Bayesian optimization. We illustrate the utility of MOO in several specific ML applications, considering objectives such as operating conditions, prediction time, sparseness, fairness, interpretability and robustness.
  arXiv  Detail & Related papers  (2022-06-15T10:23:19Z)
• Bayesian Optimization for Selecting Efficient Machine Learning Models [53.202224677485525]
  We present a unified Bayesian Optimization framework for jointly optimizing models for both prediction effectiveness and training efficiency. Experiments on model selection for recommendation tasks indicate models selected this way significantly improves model training efficiency.
  arXiv  Detail & Related papers  (2020-08-02T02:56:30Z)
• BOSH: Bayesian Optimization by Sampling Hierarchically [10.10241176664951]
  We propose a novel BO routine pairing a hierarchical Gaussian process with an information-theoretic framework to generate a growing pool of realizations. We demonstrate that BOSH provides more efficient and higher-precision optimization than standard BO across synthetic benchmarks, simulation optimization, reinforcement learning and hyper- parameter tuning tasks.
  arXiv  Detail & Related papers  (2020-07-02T07:35:49Z)

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.