Genetic Programming and Evolvable Machines最新文献

Evolving code with a large language model 使用大型语言模型演化代码

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-09-12 DOI: 10.1007/s10710-024-09494-2

Erik Hemberg, Stephen Moskal, Una-May O’Reilly

引用次数: 0

Hga-lstm: LSTM architecture and hyperparameter search by hybrid GA for air pollution prediction Hga-lstm：用于空气污染预测的 LSTM 架构和混合 GA 的超参数搜索

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-08-01 DOI: 10.1007/s10710-024-09493-3

Jiayu Liang, Yaxin Lu, Mingming Su

{"title":"Hga-lstm: LSTM architecture and hyperparameter search by hybrid GA for air pollution prediction","authors":"Jiayu Liang, Yaxin Lu, Mingming Su","doi":"10.1007/s10710-024-09493-3","DOIUrl":"https://doi.org/10.1007/s10710-024-09493-3","url":null,"abstract":"Air pollution prediction is a process of predicting the levels of air pollutants in a specific area over a given period. Since LSTM (Long Short-Term Memory) networks are particularly effective in capturing long-term dependencies and patterns in sequential data, they are widely-used for air pollution prediction. However, designing appropriate LSTM architectures and hyperparameters for given tasks can be challenging, which are normally determined by users in existing LSTM-based methods. Note that Genetic Algorithm (GA) is an effective optimization technique, and local search in augmenting the global search ability of GA has been proved, which is rarely considered by existing GA-optimzied LSTM methods. In this work, simultaneous LSTM architecture and hyperparameter search based on GA and local search techniques is investigated for air pollution prediction. Specifically, a new LSTM model search method is designed, termed as HGA-LSTM. HGA is a hybrid GA, which is proposed by integrating GA with local search adaptively. Based on HGA, HGA-LSTM is developed to search for LSTM models with simultaneous LSTM architecture and hyperparameter optimization. In HGA-LSTM, a new crossover is designed to be adaptive to the variable-length representation of LSTM models. The proposed HGA-LSTM is compared with widely-used LSTM-based and nonLSTM-based prediction methods on UCI (University of California Irvine) datasets for air pollution prediction. Results show that HGA-LSTM is generally better than both types of reference methods with its evolved LSTM models achieving lower mean square/absolute errors. Moreover, compared with a baseline method (a GA without local search), HGA-LSTM converges to lower error values, which reflects that HGA has better search ability than GA.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141882004","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey on dynamic populations in bio-inspired algorithms 生物启发算法中的动态种群调查

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-07-24 DOI: 10.1007/s10710-024-09492-4

Davide Farinati, Leonardo Vanneschi

{"title":"A survey on dynamic populations in bio-inspired algorithms","authors":"Davide Farinati, Leonardo Vanneschi","doi":"10.1007/s10710-024-09492-4","DOIUrl":"https://doi.org/10.1007/s10710-024-09492-4","url":null,"abstract":"Population-Based Bio-Inspired Algorithms (PBBIAs) are computational methods that simulate natural biological processes, such as evolution or social behaviors, to solve optimization problems. Traditionally, PBBIAs use a population of static size, set beforehand through a specific parameter. Nevertheless, for several decades now, the idea of employing populations of dynamic size, capable of adjusting during the course of a single run, has gained ground. Various methods have been introduced, ranging from simpler ones that use a predefined function to determine the population size variation, to more sophisticated methods where the population size in different phases of the evolutionary process depends on the dynamics of the evolution itself and events occurring within the population during the run. The common underlying idea in many of these approaches, is similar: to save a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141784328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming GSGP-硬件：利用 FPGA 实现几何语义遗传编程的瞬时符号回归

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-06-25 DOI: 10.1007/s10710-024-09491-5

Yazmin Maldonado, Ruben Salas, Joel A. Quevedo, Rogelio Valdez, Leonardo Trujillo

{"title":"GSGP-hardware: instantaneous symbolic regression with an FPGA implementation of geometric semantic genetic programming","authors":"Yazmin Maldonado, Ruben Salas, Joel A. Quevedo, Rogelio Valdez, Leonardo Trujillo","doi":"10.1007/s10710-024-09491-5","DOIUrl":"https://doi.org/10.1007/s10710-024-09491-5","url":null,"abstract":"Geometric Semantic Genetic Programming (GSGP) proposed an important enhancement to GP-based learning, incorporating search operators that operate directly on the semantics of the parents with bounded effects on the semantics of the offspring. This approach posed any symbolic regression fitness landscape as a unimodal function, allowing for more directed search. Moreover, it became evident that the search could be implemented in a much more efficient manner, that does not require the execution, evaluation or manipulation of variable length syntactic models. Hence, efficient implementations of this algorithm have been developed using both CPU and GPU processing. However, current implementations are still ill-suited for real-time learning, or learning on devices with limited resources, scenarios that are becoming more prevalent with the continued development of the Internet-of-Things and the increased need for efficient and distributed learning on the Edge. This paper presents GSGP-Hardware, a fully pipelined and parallel design of GSGP developed fully using VHDL, for implementation on FPGA devices. Using Vivado AMD-Xilinx for synthesis and simulation, GSGP-Hardware achieves an approximate improvement in efficiency, in terms of run time and Gpops/s, of three and four orders of magnitude, respectively, compared with the state-of-the-art GPU implementation. This is a performance increase that has not been achieved by other FPGA-based implementations of genetic programming. This is possible due to the manner in which GSGP evolves a model, and competitive accuracy is achieved by incorporating simple but powerful enhancements to the original GSGP algorithm. GSGP-Hardware allows for instantaneous symbolic regression, opening up new application domains for this powerful variant of genetic programming.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141501115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution 线性缩放的几何语义 GP：达尔文进化论与拉马克进化论

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-06-01 DOI: 10.1007/s10710-024-09488-0

Giorgia Nadizar, Berfin Sakallioglu, Fraser Garrow, Sara Silva, Leonardo Vanneschi

{"title":"Geometric semantic GP with linear scaling: Darwinian versus Lamarckian evolution","authors":"Giorgia Nadizar, Berfin Sakallioglu, Fraser Garrow, Sara Silva, Leonardo Vanneschi","doi":"10.1007/s10710-024-09488-0","DOIUrl":"https://doi.org/10.1007/s10710-024-09488-0","url":null,"abstract":"Geometric Semantic Genetic Programming (GSGP) has shown notable success in symbolic regression with the introduction of Linear Scaling (LS). This achievement stems from the synergy of the geometric semantic genetic operators of GSGP with the scaling of the individuals for computing their fitness, which favours programs with a promising behaviour. However, the initial combination of GSGP and LS (GSGP-LS) underutilised the potential of LS, scaling individuals only for fitness evaluation, neglecting to incorporate improvements into their genetic material. In this paper we propose an advancement, GSGP with Lamarckian LS (GSGP-LLS), wherein we update the individuals in the population with their scaling coefficients in a Lamarckian fashion, i.e., by inheritance of acquired traits. We assess GSGP-LS and GSGP-LLS against standard GSGP for the task of symbolic regression on five hand-tailored benchmarks and six real-life problems. On the former ones, GSGP-LS and GSGP-LLS both consistently improve GSGP, though with no clear global superiority between them. On the real-world problems, instead, GSGP-LLS steadily outperforms GSGP-LS, achieving faster convergence and superior final performance. Notably, even in cases where LS induces overfitting on challenging problems, GSGP-LLS surpasses GSGP-LS, due to its slower and more localised optimisation steps.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141195445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benjamin Doerr and Frank Neumann (editors): theory of evolutionary computation 本杰明-多尔和弗兰克-诺伊曼（主编）：进化计算理论

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-05-24 DOI: 10.1007/s10710-024-09490-6

Jonathan E. Rowe

引用次数: 0

Hierarchical non-dominated sort: analysis and improvement 分层非支配排序：分析与改进

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-04-16 DOI: 10.1007/s10710-024-09487-1

Ved Prakash, Sumit Mishra

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A new representation in 3D VLSI floorplan: 3D O-Tree 三维超大规模集成电路平面图的新表示方法：3D O-Tree

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-04-01 DOI: 10.1007/s10710-024-09485-3

Rohin Gupta, Sandeep Singh Gill

引用次数: 0

Architecture search of accurate and lightweight CNNs using genetic algorithm 使用遗传算法搜索精确轻量级 CNN 的架构

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-04-01 DOI: 10.1007/s10710-024-09484-4

Jiayu Liang, Hanqi Cao, Yaxin Lu, Mingming Su

引用次数: 0

A genetic algorithm for rule extraction in fuzzy adaptive learning control networks 模糊自适应学习控制网络中规则提取的遗传算法

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2024-03-30 DOI: 10.1007/s10710-024-09486-2

Glender Brás, Alisson Marques Silva, Elizabeth F. Wanner

引用次数: 0