In this paper, we formulate the hyperparameter tuning problem in machine
learning as a bilevel program. The bilevel program is solved using a micro
genetic algorithm that is enhanced with a linear program. While the genetic
algorithm searches over discrete hyperparameters, the linear program
enhancement allows hyper local search over continuous hyperparameters. The
major contribution in this paper is the formulation of a linear program that
supports fast search over continuous hyperparameters, and can be integrated
with any hyperparameter search technique. It can also be applied directly on
any trained machine learning or deep learning model for the purpose of
fine-tuning. We test the performance of the proposed approach on two datasets,
MNIST and CIFAR-10. Our results clearly demonstrate that using the linear
program enhancement offers significant promise when incorporated with any
population-based approach for hyperparameter tuning.

本文将机器学习中的超参数调优问题建模为一个双层规划问题，利用一种线性规划改进的微遗传算法来解决该问题。主要贡献是提出了一个支持快速搜索连续超参数的线性规划，并可以与任何超参数搜索技术相结合，对任何训练好的机器学习或深度学习模型进行微调。通过在 MNIST 和 CIFAR-10 两个数据集上进行测试，结果明确表明与任何基于群体的超参数调优方法相结合时，使用线性规划改进具有显著的潜力。

机器学习中的超参数调优的线性规划增强遗传算法

A Linear Programming Enhanced Genetic Algorithm for Hyperparameter  Tuning in Machine Learning

Deep Reinforcement Learning (DRL) algorithms have been successfully applied
to a range of challenging control tasks. However, these methods typically
suffer from three core difficulties: temporal credit assignment with sparse
rewards, lack of effective exploration, and brittle convergence properties that
are extremely sensitive to hyperparameters. Collectively, these challenges
severely limit the applicability of these approaches to real-world problems.
Evolutionary Algorithms (EAs), a class of black box optimization techniques
inspired by natural evolution, are well suited to address each of these three
challenges. However, EAs typically suffer from high sample complexity and
struggle to solve problems that require optimization of a large number of
parameters. In this paper, we introduce Evolutionary Reinforcement Learning
(ERL), a hybrid algorithm that leverages the population of an EA to provide
diversified data to train an RL agent, and reinserts the RL agent into the EA
population periodically to inject gradient information into the EA. ERL
inherits EA's ability of temporal credit assignment with a fitness metric,
effective exploration with a diverse set of policies, and stability of a
population-based approach and complements it with off-policy DRL's ability to
leverage gradients for higher sample efficiency and faster learning.
Experiments in a range of challenging continuous control benchmarks demonstrate
that ERL significantly outperforms prior DRL and EA methods.

本文介绍了一种基于人工进化算法和深度强化学习相结合的进化增强学习算法，该算法克服了传统深度强化学习算法中时序资格分配、探索效率和收敛性极度敏感的问题，使用基于种群的进化算法来训练深度强化学习代理人，实验结果表明，该方法在多种连续控制基准测试中显著优于传统深度强化学习和进化算法方法。

演化引导的强化学习策略梯度

Evolution-Guided Policy Gradient in Reinforcement Learning

Automatic design with machine learning and molecular simulations has shown a
remarkable ability to generate new and promising drug candidates. Current
models, however, still have problems in simulation concurrency and molecular
diversity. Most methods generate one molecule at a time and do not allow
multiple simulators to run simultaneously. Additionally, better molecular
diversity could boost the success rate in the subsequent drug discovery
process. We propose a new population-based approach using grammatical evolution
named ChemGE. In our method, a large population of molecules are updated
concurrently and evaluated by multiple simulators in parallel. In docking
experiments with thymidine kinase, ChemGE succeeded in generating hundreds of
high-affinity molecules whose diversity is better than that of known inding
molecules in DUD-E.

通过机器学习和分子模拟进行的自动设计已经表现出生成新的和有前途的新药物候选物的显着能力。本文提出了使用语法进化的一种新的基于群体的方法。在 thymidine 激酶的对接实验中，ChemGE 成功生成了数百个高亲和力的分子，其多样性优于 DUD-E 中已知的分子。