贝叶斯优化算法中的适应度继承

Protein Sequence Design with Batch Bayesian Optimisation

Protein sequence design is a challenging problem in protein engineering, which aims to discover novel proteins with useful biological functions. Directed evolution is a widely-used approach for protein sequence design, which mimics the evolution cycle in a laboratory environment and conducts an iterative protocol. However, the burden of laboratory experiments can be reduced by using machine learning approaches to build a surrogate model of the protein landscape and conducting in-silico population selection through model-based fitness prediction. In this paper, we propose a new method based on …

Inheritance-Based Diversity Measures for Explicit Convergence Control in Evolutionary Algorithms

Diversity is an important factor in evolutionary algorithms to prevent premature convergence towards a single local optimum. In order to maintain diversity throughout the process of evolution, various means exist in literature. We analyze approaches to diversity that (a) have an explicit and quantifiable influence on fitness at the individual level and (b) require no (or very little) additional domain knowledge such as domain-specific distance functions. We also introduce the concept of genealogical diversity in a broader study. We show that employing these approaches can help evolutionary alg…

AutoHLS：学习加速HLS设计的设计空间探索

AutoHLS: Learning to Accelerate Design Space Exploration for HLS Designs

AutoHLS框架结合深度神经网络与贝叶斯优化，加速HLS设计空间探索，实现高达70倍探索时间加速。

The Effect of Epigenetic Blocking on Dynamic Multi-Objective Optimisation Problems

Hundreds of Evolutionary Computation approaches have been reported. From an evolutionary perspective they focus on two fundamental mechanisms: cultural inheritance in Swarm Intelligence and genetic inheritance in Evolutionary Algorithms. Contemporary evolutionary biology looks beyond genetic inheritance, proposing a so-called ``Extended Evolutionary Synthesis''. Many concepts from the Extended Evolutionary Synthesis have been left out of Evolutionary Computation as interest has moved toward specific implementations of the same general mechanisms. One such concept is epigenetic inheritance, whi…

为何风险对蛋白质结合剂设计至关重要

Why risk matters for protein binder design

本文从风险和成本约束视角，在11个蛋白质结合剂适应度景观上比较了72种贝叶斯优化模型组合，发现风险-性能轴上存在Pareto最优模型，且景观属性与模型性能稳健相关，强调严格模型选择需大量计算统计工作。

Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive…

上下文引导：通过数值基础模型学习少样本多任务优化的任务间协同

In-Context Guidance: Learning Inter-Task Synergies via Numerical Foundational Models for Few-Shot Multitask Optimization

提出ICG-MTO框架，利用冻结数值基础模型通过上下文学习推断引导信号，改进少样本多任务优化中的任务间耦合估计，在合成和真实问题上验证了有效性与通用性。

一种用于可合成分子空间导航的遗传算法

A Genetic Algorithm for Navigating Synthesizable Molecular Spaces

SynGA是一种用于可合成分子空间导航的遗传算法，通过自定义交叉和变异算子确保合成可行性，并结合机器学习过滤器提升性能，适用于属性优化等任务。

适应度景观足迹：一个比较神经架构搜索问题的框架

Fitness Landscape Footprint: A Framework to Compare Neural Architecture Search Problems

本文提出由八项通用指标构成的 fitness landscape footprint，用适应度景观分析定量刻画并比较 NAS 问题的难度与结构特征。

通过预融合边剪枝实现易处理贝叶斯网络融合的遗传算法

Genetic Algorithms for Tractable Bayesian Network Fusion via Pre-Fusion Edge Pruning

针对贝叶斯网络融合中依赖保留与计算易处理性的平衡问题，提出带定制化设计的遗传算法，实验显示其性能优于适配方法与贪心基线。

Denoising Autoencoders for fast Combinatorial Black Box Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is…

Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization

Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and with problem complexity. The results are compared to the Bayesian Optimization Algorithm, a state-of-the-art EDA. Although RBM-EDA req…

护士排班问题的贝叶斯优化算法

A Bayesian Optimisation Algorithm for the Nurse Scheduling Problem

针对护士排班问题，提出了一种贝叶斯优化算法，该算法针对每个护士的排班任务，从一个集合中选择一个合适的排班规则。与我们以前使用Gas实现隐式学习的工作不同，该算法中的学习是显式的，即。最终，我们将能够直接识别和混合构建块。贝叶斯优化算法被应用到实现这样的显式学习，通过建立一个贝叶斯网络的联合分布的解决方案。网络中每个变量的条件概率是根据一组初始的有希望的解决方案来计算的。随后，生成每个变量的每个新实例，即在我们的情况下，获得了一个新的规则字符串。另一组规则字符串将以这种方式生成，其中一些将基于适应度选择替换先前的字符串。如果不满足停止条件，贝叶斯网络中所有节点的条件概率将使用当前的有希望规则串集再次更新。52个真实数据实例的计算结果表明了该方法的成功。此外，该方法的学习机制也适用于其他调度问题。

Accelerating the Evolutionary Algorithms by Gaussian Process Regression with $ε$-greedy acquisition function

In this paper, we propose a novel method to estimate the elite individual to accelerate the convergence of optimization. Inspired by the Bayesian Optimization Algorithm (BOA), the Gaussian Process Regression (GPR) is applied to approximate the fitness landscape of original problems based on every generation of optimization. And simple but efficient $ε$-greedy acquisition function is employed to find a promising solution in the surrogate model. Proximity Optimal Principle (POP) states that well-performed solutions have a similar structure, and there is a high probability of better solutions exi…