Black-box optimizationLearning to optimizeMeta-learningRecurrent neural networksConstrained optimizationRecently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks including derivative-free ...
黑盒优化面临最大的问题就是“收敛性”问题,它是不依赖梯度的(Derivative Free Optimization Approach),只关心输入的动作和输出的奖励,如何保证收敛性?讨论收敛性问题显然超出了本文的讨论范畴,庆幸的是我们生活的宇宙恰好能够使这类算法几乎收敛,这种优化方法跟进化算法(EA)十分相似,而且 worksembarrassinglywell 。 OK,...
Black-box optimization for Julia. Contribute to robertfeldt/BlackBoxOptim.jl development by creating an account on GitHub.
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual...
在输入一个Prompt A之后,是否有方法可以自动优化Prompt A,得到PromptA′,然后将PromptA′喂给大模型,使得大模型产生的效果更好 背景介绍 动机 随着LLM模型越来越大,调整它们以遵循用户意图和指令变得越来越困难 目前的方法大多数基于强化学习从人类反馈进行调参。但是这些方法效率低下,对模型可解释性差,且需要可训练...
This code provides a platform to benchmark and compare continuous optimizers, AKA non-linear solvers for numerical optimization. It is fully written in ANSI C and Python (reimplementing the original Comparing Continous Optimizer platform) with other languages calling the C code. Languages currently av...
This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual...
Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search 发表时间:2020(NeurIPS 2020) 文章要点:我们知道贝叶斯优化做到高维的时候计算量很大,根本算不出来。这篇文章是把MCTS和贝叶斯优化结合起来,做高维的优化问题。主要思路是先用MCTS分割搜索空间,然后在子空间上再用贝叶斯优化去...
Large-Scale Black-Box Optimization Cooperative Co-evolutionDynamicDifferential GroupingCooperative Co-evolution (CC) framework is an important method to tackle Large Scale Black-Box Optimization (LSBO) problem. One of the main step in CC is grouping for the decision variables, which affects the ...
先拿一个好的prompt和一个坏的prompt 然后告知大模型,让大模型比较两者的区别,并且告知操作者,该怎样写更好,然后再给出一个原始的prompt让大模型依据它先前的判断进行修改,这样就得到一个原始prompt和大模型优化后prompt,然后就可以拿这个原始的作为输入,优化后的作为输出,训练一个prompt修改器。 以上,就是如果让...