检索结果-内蒙古大学图书馆

Enhancing Program Synthesis with Large Language Models Using Many-Objective grammar-guided genetic programming

ALGORITHMS 2024年第7期17卷 287页

作者： Tao, Ning Ventresque, Anthony Nallur, Vivek Saber, Takfarinas Univ Coll Dublin Sch Comp Sci Dublin D04 V1W8 Ireland Trinity Coll Dublin Sch Comp Sci & Stat Dublin D02 PN40 Ireland Univ Galway Sch Comp Sci Galway H91 TK33 Ireland SFI Lero Irish Software Res Ctr Limerick V94 NYD3 Ireland

The ability to automatically generate code, i.e., program synthesis, is one of the most important applications of artificial intelligence (AI). Currently, two AI techniques are leading the way: large language models (LLMs) and genetic programming (GP) methods-each with its strengths and weaknesses. While LLMs have shown success in program synthesis from a task description, they often struggle to generate the correct code due to ambiguity in task specifications, complex programming syntax, and lack of reliability in the generated code. Furthermore, their generative nature limits their ability to fix erroneous code with iterative LLM prompting. grammar-guided genetic programming (G3P, i.e., one of the top GP methods) has been shown capable of evolving programs that fit a defined Backus-Naur-form (BNF) grammar based on a set of input/output tests that help guide the search process while ensuring that the generated code does not include calls to untrustworthy libraries or poorly structured snippets. However, G3P still faces issues generating code for complex tasks. A recent study attempting to combine both approaches (G3P and LLMs) by seeding an LLM-generated program into the initial population of the G3P has shown promising results. However, the approach rapidly loses the seeded information over the evolutionary process, which hinders its performance. In this work, we propose combining an LLM (specifically ChatGPT) with a many-objective G3P (MaOG3P) framework in two parts: (i) provide the LLM-generated code as a seed to the evolutionary process following a grammar-mapping phase that creates an avenue for program evolution and error correction;and (ii) leverage many-objective similarity measures towards the LLM-generated code to guide the search process throughout the evolution. The idea behind using the similarity measures is that the LLM-generated code is likely to be close to the correct fitting code. Our approach compels any generated program to adhere to the BNF gr

关键词： program synthesis large language models grammar-guided genetic programming grammar multi-objective

来源：评论

学校读者我要写书评

暂无评论

Grammatically uniform population initialization for grammar-guided genetic programming

引用

SOFT COMPUTING 2020年第15期24卷 11265-11282页

作者： Ramos Criado, Pablo Barrios Rolania, D. Manrique, Daniel Serrano, Emilio Aturing Res Salamanca Spain Univ Politecn Madrid Dept Matemat Aplicada Ingn Ind Madrid Spain Univ Politecn Madrid Dept Inteligencia Artificial Madrid Spain

The initial population distribution is an essential issue in evolutionary computation performance. Population initialization methods for grammar-guided genetic programming have some difficulties generating a representative sample of the search space, which negatively affects the overall evolutionary process. This paper presents a grammatically uniform population initialization method to address this issue by improving the initial population uniformity: the equiprobability of obtaining any individual of the search space defined by the context-free grammar. The proposed initialization method assigns and updates probabilities dynamically to the production rules of the grammar to pursue uniformity and includes a code bloat control mechanism. We have conducted empirical experiments to compare the proposed algorithm with a standard initialization approach very often used in grammar-guided genetic programming. The results report that the proposed initialization method approximates very well a uniform distribution of the individuals in the search space. Moreover, the overall evolutionary process that takes place after the population initialization performs better in terms of convergence speed and quality of the final solutions achieved when the proposed method generates the initial population than when the usual approach does. The results also show that these performance differences are more significant when the experiments involve large search spaces.

关键词： grammar-guided genetic programming Initialization Genotypic uniformity Stochastic context-free grammar

来源：评论

学校读者我要写书评

暂无评论

A propositionalization method of multi-relational data based on grammar-guided genetic programming

引用

EXPERT SYSTEMS WITH APPLICATIONS 2021年 168卷 114263-114263页

作者： Quintero-Dominguez, Luis A. Morell, Carlos Ventura, Sebastian Univ Sancti Spiritus Jose Marti Perez Comp Engn Dept Comandante Fajardo S-N Sancti Spiritus 60100 Cuba Univ Cent Marta Abreu Las Villas Comp Sci Dept Santa Clara Cuba Univ Cordoba Dept Comp Sci & Numer Anal Cordoba Spain Maimonides Inst Biomed Res Cordoba IMIBIC Cordoba Spain

The propositionalization process tries to find distinctive features of the examples in a database to transform such relational data into a simpler representation. More informative features have a positive impact on the classification capabilities of the learning algorithms. In this work, we propose a new propositionalization method, which generates complex Boolean attributes using grammar-guided genetic programming (G3P). The generated attributes are compound formulas that combine word items coming from a Bag-of-Words (BoW) representation using Boolean operators. The proposal was assessed against three state-of-the-art simple-instance and multiple-instance propositionalization methods. The experimental results show that the proposed method achieves an improvement in terms of classification accuracy and a considerable reduction in the dimensionality of the resulting datasets.

关键词： G3Prop Multi-Relational Data Mining Propositionalization Bag-of-Words grammar-guided genetic programming

来源：评论

学校读者我要写书评

暂无评论

Evolving Better Rerouting Surrogate Travel Costs with grammar-guided genetic programming

Evolving Better Rerouting Surrogate Travel Costs with Gramma...

引用

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Saber, Takfarinas Wang, Shen Univ Coll Dublin Sch Comp Sci Dublin Ireland

ISBN: (纸本)9781728169293

The number of drivers using on-board systems to navigate through urban areas is increasing. Drivers get real time information regarding traffic conditions and change their routes accordingly. Adapting a route clearly enables drivers to avoid closed roads or circumvent major hotspots. However, given the non-linearity of the traffic dynamics in urban environments, choosing a route based only on current traffic load or current average vehicle speed is not a guaranty of a lower overall travel time. In this work, we design an evolutionary system to search for better surrogate travel cost that drivers could optimise in their rerouting to achieve better overall travel times. Our system uses the grammar-guided genetic programming algorithm to evolve surrogate travel cost expressions and evaluate their performances on a micro traffic simulator. Our system is able to evolve different expressions that meet characteristics of specific urban environments instead of a one size fits all expression. We have seen in our experimental study on a traffic scenario representing Dublin city centre that our system is able to evolve surrogate travel cost expressions with similar to 34% and similar to 10% improvements in average travel time over the no rerouting and the average travel speed based rerouting algorithms.

关键词： Evolution Computation grammar-guided genetic programming Simulation of Urban MObility Traffic Rerouting Surrogate Travel Cost

来源：评论

学校读者我要写书评

暂无评论

A multi-level grammar approach to grammar-guided genetic programming: the case of scheduling in heterogeneous networks

引用

genetic programming AND EVOLVABLE MACHINES 2019年第2期20卷 245-283页

作者： Saber, Takfarinas Fagan, David Lynch, David Kucera, Stepan Claussen, Holger O'Neill, Michael Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Coll Dublin Sch Business Nat Comp Res & Applicat Grp Dublin Ireland Nokia Bell Labs Dublin Ireland

The scale at which the human race consumes data has increased exponentially in recent years. One key part in this increase has been the usage of smart phones and connected devices by the populous. Multi-level heterogeneous networks are the driving force behind this mobile revolution, but these are constrained with limited bandwidth and over-subscription. Scheduling users on these networks has become a growing issue. In recent years grammar-guided genetic programming (G3P) has shown its capability to evolve beyond human-competitive network schedulers. Despite the performance of the G3P schedulers, a large margin of improvement is demonstrated to still exist. In the pursuit of this goal we recently proposed a multi-level grammar approach to generating schedulers. The complexity of the grammar was increased at various stages during evolution, allowing for individuals to add more complex functions through variation operations. The goal is to evolve good quality solutions before allowing the population to specialise more as the grammar functionality increased in a layered learning way. In this paper the results of this initial study are replicated, and confirmed, and it is seen that this approach improves the quality of the evolved schedulers. However, despite the gain in performance, we notice that the proposed approach comes with an acute sensitivity to the generation at which the grammar complexity is increased. Therefore, we put forward a novel seeding strategy and show that the seeding strategy mitigates the shortcomings of the original approach. The use of the seeding strategy outperforms the original approach in all the studied cases, and thus yields a better overall performance than the state-of-the-art G3P generated schedulers.

关键词： Telecommunication Heterogeneous network Scheduling grammar-guided genetic programming Multi-level grammar Seeding

来源：评论

学校读者我要写书评

暂无评论

Generation of Particle Swarm Optimization algorithms: An experimental study using grammar-guided genetic programming

引用

APPLIED SOFT COMPUTING 2017年 60卷 281-296页

作者： Miranda, Pericles B. C. Prudencio, Ricardo B. C. Rural Fed Univ Pernambuco Dept Stat & Informat Recife PE Brazil Rural Fed Univ Pernambuco Ctr Informat Recife PE Brazil

Particle Swarm Optimization (PSO) is largely used to solve optimization problems effectively. Nonetheless, the PSO performance depends on the fine tuning of different parameters. To make the algorithm design process more independent from human intervention, some researchers have treated this task as an optimization problem. grammar-guided genetic programming (GGGP) algorithms, in particular, have been widely studied and applied in the context of algorithm optimization. GGGP algorithms produce customized designs based on a set of production rules defined in the grammar, differently from methods that simply select designs in a pre-defined limited search space. Although GGGP algorithms have been largely used in other contexts, they have not been deeply investigated in the generation of PSO algorithms. Thus, this work applies GGGP algorithms in the context of PSO algorithm design problem. Herein, we performed an experimental study comparing different GGGP approaches for the generation of PSO algorithms. The main goal is to perform a deep investigation aiming to identify pros and cons of each approach in the current task. In the experiments, a comparison between a tree-based GGGP approach and commonly used linear GGGP approaches for the generation of PSO algorithms was performed. The results showed that the tree-based GGGP produced better algorithms than the counterparts. We also compared the algorithms generated by the tree-based technique to state-of-the-art optimization algorithms, and it achieved competitive results. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Generation hyper-heuristics grammar-guided genetic programming Algorithm design Particle Swarm Optimization

来源：评论

学校读者我要写书评

暂无评论

Biological insights on grammar-structured mutations improve fitness and diversity 23

Biological insights on grammar-structured mutations improve ...

引用

genetic and Evolutionary Computation Conference (GECCO)

作者： Tiso, Stefano Carvalho, Pedro Lourenco, Nuno Machado, Penousal Univ Groningen Groningen Netherlands Univ Coimbra Coimbra Portugal

ISBN: (纸本)9798400701191

grammar-guided genetic programming (GGGP) employs a variety of concepts from evolutionary theory to autonomously design solutions for a given task. Recent insights from evolutionary biology can lead to further improvements in GGGP algorithms. In this paper, we propose a new mutation approach called Facilitated Mutation (FM) that is based on the theory of Facilitated Variation. We evaluate the performance of FM on the evolution of neural network optimizers for image classification, a relevant task in Evolutionary Computation, with important implications for the field of Machine Learning. We compare FM and FM combined with crossover (FMX) against a typical mutation approach to assess the benefits of the approach. We find that FMX provides statistical improvements in key metrics, creating a superior optimizer overall (+0.5% average test accuracy), improving the average quality of solutions (+53% average population fitness), and discovering more diverse high-quality behaviors (+523 high-quality solutions discovered on average). Additionally, FM and FMX reduce the number of fitness evaluations in an evolutionary run, reducing computational costs. FM's implementation cost is minimal and the approach is theoretically applicable to any algorithm where genes are associated with a grammar non-terminal, making this approach applicable in many existing GGGP systems.

关键词： grammar-guided genetic programming Mutation Optimizers

来源：评论

学校读者我要写书评

暂无评论

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

引用

KNOWLEDGE AND INFORMATION SYSTEMS 2012年第1期32卷 53-76页

作者： Luna, Jose M. Romero, Jose Raul Ventura, Sebastian Univ Cordoba Dept Comp Sci & Numer Anal E-14071 Cordoba Spain

This paper presents a proposal for the extraction of association rules called G3PARM (grammar-guided genetic programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.

关键词： Association rules genetic programming grammar-guided genetic programming Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

grammar-based evolutionary approach for automated workflow composition with domain-specific operators and ensemble diversity

引用

APPLIED SOFT COMPUTING 2024年 153卷

作者： Barbudo, Rafael Ramirez, Aurora Romero, Jose Raul Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain Andalusian Res Inst Data Sci & Computat Intelligen Cordoba Spain

The process of extracting valuable and novel insights from raw data involves a series of complex steps. In the realm of Automated Machine Learning (AutoML), a significant research focus is on automating aspects of this process, specifically tasks like selecting algorithms and optimising their hyper -parameters. A particularly challenging task in AutoML is automatic workflow composition (AWC). AWC aims to identify the most effective sequence of data preprocessing and machine learning algorithms, coupled with their best hyper -parameters, for a specific dataset. However, existing AWC methods are limited in how many and in what ways they can combine algorithms within a workflow. Addressing this gap, this paper introduces EvoFrow, a grammar -based evolutionary approach for AWC. EvoFrow enhances the flexibility in designing workflow structures, empowering practitioners to select algorithms that best fit their specific requirements. EvoFrow stands out by integrating two innovative features. First, it employs a suite of genetic operators, designed specifically for AWC, to optimise both the structure of workflows and their hyper -parameters. Second, it implements a novel updating mechanism that enriches the variety of predictions made by different workflows. Promoting this diversity helps prevent the algorithm from overfitting. With this aim, EvoFrow builds an ensemble whose workflows differ in their misclassified instances. To evaluate EvoFrow's effectiveness, we carried out empirical validation using a set of classification benchmarks. We begin with an ablation study to demonstrate the enhanced performance attributable to EvoFrow's unique components. Then, we compare EvoFrow with other AWC approaches, encompassing both evolutionary and non -evolutionary techniques. Our findings show that EvoFrow's specialised genetic operators and updating mechanism substantially outperform current leading methods in predictive performance. Additionally, EvoFrow is capable of discovering

关键词： AutoML Automated workflow composition Algorithm selection Hyper-parameter optimisation grammar-guided genetic programming Ensemble learning Classification

来源：评论

学校读者我要写书评

暂无评论

EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods

引用

genetic programming AND EVOLVABLE MACHINES 2021年第4期22卷 511-537页

作者： Schmitt, Jonas Kuckuk, Sebastian Koestler, Harald Friedrich Alexander Univ Erlangen Nurnberg Chair Syst Simulat Erlangen Germany

For many systems of linear equations that arise from the discretization of partial differential equations, the construction of an efficient multigrid solver is challenging. Here we present EvoStencils, a novel approach for optimizing geometric multigrid methods with grammar-guided genetic programming, a stochastic program optimization technique inspired by the principle of natural evolution. A multigrid solver is represented as a tree of mathematical expressions that we generate based on a formal grammar. The quality of each solver is evaluated in terms of convergence and compute performance by automatically generating an optimized implementation using code generation that is then executed on the target platform to measure all relevant performance metrics. Based on this, a multi-objective optimization is performed using a non-dominated sorting-based selection. To evaluate a large number of solvers in parallel, they are distributed to multiple compute nodes. We demonstrate the effectiveness of our implementation by constructing geometric multigrid solvers that are able to outperform hand-crafted methods for Poisson's equation and a linear elastic boundary value problem with up to 16 million unknowns on multi-core processors with Ivy Bridge and Broadwell microarchitecture.

关键词： grammar-guided genetic programming Multigrid methods Algorithm design Code generation Distributed computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：