Genetic Programming and Evolvable Machines最新文献_第8页

Automatic generation of regular expressions for the Regex Golf challenge using a local search algorithm 使用本地搜索算法为Regex Golf挑战自动生成正则表达式

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-01 DOI: 10.1007/s10710-021-09411-x

André de Almeida Farzat, Márcio de Oliveira Barros

引用次数: 1

Graph representations in genetic programming 遗传规划中的图表示

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-09-30 DOI: 10.1007/s10710-021-09413-9

Françoso Dal Piccol Sotto, Léo, Kaufmann, Paul, Atkinson, Timothy, Kalkreuth, Roman, Porto Basgalupp, Márcio

{"title":"Graph representations in genetic programming","authors":"Françoso Dal Piccol Sotto, Léo, Kaufmann, Paul, Atkinson, Timothy, Kalkreuth, Roman, Porto Basgalupp, Márcio","doi":"10.1007/s10710-021-09413-9","DOIUrl":"https://doi.org/10.1007/s10710-021-09413-9","url":null,"abstract":"Graph representations promise several desirable properties for genetic programming (GP); multiple-output programs, natural representations of code reuse and, in many cases, an innate mechanism for neutral drift. Each graph GP technique provides a program representation, genetic operators and overarching evolutionary algorithm. This makes it difficult to identify the individual causes of empirical differences, both between these methods and in comparison to traditional GP. In this work, we empirically study the behaviour of Cartesian genetic programming (CGP), linear genetic programming (LGP), evolving graphs by graph programming and traditional GP. By fixing some aspects of the configurations, we study the performance of each graph GP method and GP in combination with three different EAs: generational, steady-state and ((1+lambda )). In general, we find that the best choice of representation, genetic operator and evolutionary algorithm depends on the problem domain. Further, we find that graph GP methods can increase search performance on complex real-world regression problems and, particularly in combination with the ((1 + lambda)) EA, are significantly better on digital circuit synthesis tasks. We further show that the reuse of intermediate results by tuning LGP’s number of registers and CGP’s levels back parameter is of utmost importance and contributes significantly to better convergence of an optimization algorithm when solving complex problems that benefit from code reuse.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"30 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496050","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: 9

Relationships between parent selection methods, looping constructs, and success rate in genetic programming 遗传程序设计中亲本选择方法、循环结构和成功率之间的关系

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-09-30 DOI: 10.1007/s10710-021-09417-5

A. Saini, L. Spector

引用次数: 1

EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods EvoStencils:一种基于语法的遗传规划方法，用于构造高效的几何多网格方法

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-09-03 DOI: 10.1007/s10710-021-09412-w

J. Schmitt, S. Kuckuk, H. Köstler

引用次数: 4

Software review: Pony GE2. 软件评审:Pony GE2。

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-09-01 Epub Date: 2021-07-22 DOI: 10.1007/s10710-021-09409-5

Tuong Manh Vu

引用次数: 2

Genetic programming convergence 遗传规划收敛

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-08-30 DOI: 10.1007/s10710-021-09405-9

W. Langdon

引用次数: 6

Genetic programming convergence 遗传规划收敛

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-08-30 DOI: 10.1145/3520304.3534063

W. Langdon

引用次数: 10

Constant optimization and feature standardization in multiobjective genetic programming 多目标遗传规划中的持续优化与特征标准化

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-08-19 DOI: 10.1007/s10710-021-09410-y

Rockett, Peter

{"title":"Constant optimization and feature standardization in multiobjective genetic programming","authors":"Rockett, Peter","doi":"10.1007/s10710-021-09410-y","DOIUrl":"https://doi.org/10.1007/s10710-021-09410-y","url":null,"abstract":"This paper extends the numerical tuning of tree constants in genetic programming (GP) to the multiobjective domain. Using ten real-world benchmark regression datasets and employing Bayesian comparison procedures, we first consider the effects of feature standardization (without constant tuning) and conclude that standardization generally produces lower test errors, but, contrary to other recently published work, we find much less clear trend for tree sizes. In addition, we consider the effects of constant tuning – with and without feature standardization – and observe that (1) constant tuning invariably improves test error, and (2) usually decreases tree size. Combined with standardization, constant tuning produces the best test error results; tree sizes, however, are increased. We also examine the effects of applying constant tuning only once at the end a conventional GP run which turns out to be surprisingly promising. Finally, we consider the merits of using numerical procedures to tune tree constants and observe that for around half the datasets evolutionary search alone is superior whereas for the remaining half, parameter tuning is superior. We identify a number of open research questions that arise from this work.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"30 6","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496049","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: 1

Matchmaker, matchmaker, make me a match: geometric, variational, and evolutionary implications of criteria for tag affinity 媒人，媒人，让我匹配：标签亲和力标准的几何、变分和进化含义

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-08-10 DOI: 10.1007/s10710-023-09448-0

M. Moreno, Alexander Lalejini, C. Ofria

引用次数: 2

Inference of time series components by online co-evolution 基于在线协同进化的时间序列成分推断

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-07-21 DOI: 10.1007/s10710-021-09408-6

Danil Koryakin, S. Otte, Martin Volker Butz

引用次数: 0