Genetic Programming and Evolvable Machines最新文献_第7页

Complexity and aesthetics in generative and evolutionary art 生成与进化艺术中的复杂性与美学

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2022-01-05 DOI: 10.1007/s10710-022-09429-9

J. Mccormack, Camilo Cruz Gambardella

引用次数: 3

Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings 遗传规划:第25届欧洲会议，EuroGP 2022，作为EvoStar 2022的一部分，马德里，西班牙，2022年4月20日至22日，会议记录

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2022-01-01 DOI: 10.1007/978-3-031-02056-8

引用次数: 0

Editorial Introduction. 编辑介绍。

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2022-01-01 Epub Date: 2022-08-20 DOI: 10.1007/s10710-022-09437-9

Leonardo Trujillo, Ting Hu, Nuno Lourenço, Mengjie Zhang

引用次数: 0

Genetic programming for iterative numerical methods 迭代数值方法的遗传规划

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-11-25 DOI: 10.1007/s10710-021-09425-5

Dominik Sobania, J. Schmitt, H. Köstler, Franz Rothlauf

引用次数: 0

Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios 使用多目标语法进化的血糖预测：“不可知论”和“假设”情景分析

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-11-18 DOI: 10.1007/s10710-021-09424-6

Sergio Contador, J. Colmenar, O. Garnica, J. M. Velasco, J. Hidalgo

引用次数: 5

Artificial intelligence for fashion, Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8 how AI is revolutionizing the fashion industry 人工智能的时尚，Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8人工智能如何彻底改变时尚产业

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-26 DOI: 10.1007/s10710-021-09422-8

Grace Buttler

引用次数: 0

Robert Elliott Smith: Rage Inside the Machine—the prejudice of algorithms, and how to stop the internet making bigots of us all 罗伯特·艾略特·史密斯:机器内部的愤怒——算法的偏见，以及如何阻止互联网使我们所有人变得偏执

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-25 DOI: 10.1007/s10710-021-09420-w

Walid Magdy

引用次数: 2

Generating networks of genetic processors 生成基因处理器网络

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-21 DOI: 10.1007/s10710-021-09423-7

Campos, Marcelino, Sempere, José M.

{"title":"Generating networks of genetic processors","authors":"Campos, Marcelino, Sempere, José M.","doi":"10.1007/s10710-021-09423-7","DOIUrl":"https://doi.org/10.1007/s10710-021-09423-7","url":null,"abstract":"The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"29 4","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496055","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 semantic genetic programming framework based on dynamic targets 基于动态目标的语义遗传规划框架

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-05 DOI: 10.1007/s10710-021-09419-3

Stefano Ruberto, Valerio Terragni, Jason H. Moore

引用次数: 1

Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design 进化电路设计中笛卡儿遗传规划的语义导向突变算子

IF 2.6 3区计算机科学

Genetic Programming and Evolvable Machines Pub Date : 2021-10-02 DOI: 10.1007/s10710-021-09416-6

Hodan, David, Mrazek, Vojtech, Vasicek, Zdenek

{"title":"Semantically-oriented mutation operator in cartesian genetic programming for evolutionary circuit design","authors":"Hodan, David, Mrazek, Vojtech, Vasicek, Zdenek","doi":"10.1007/s10710-021-09416-6","DOIUrl":"https://doi.org/10.1007/s10710-021-09416-6","url":null,"abstract":"Cartesian genetic programming (CGP) represents the most efficient method for the evolution of digital circuits. Despite many successful applications, however, CGP suffers from limited scalability, especially when used for evolutionary circuit design, i.e. design of circuits from a randomly initialized population. Considering the multiplier design problem, for example, the 5(times)5-bit multiplier represents the most complex circuit designed by the evolution from scratch. The efficiency of CGP highly depends on the performance of the point mutation operator, however, this operator is purely stochastic. This contrasts with the recent developments in genetic programming (GP), where advanced informed approaches such as semantic-aware operators are incorporated to improve the search space exploration capability of GP. In this paper, we propose a semantically-oriented mutation operator ((mathrm {SOMO}^k)) suitable for the evolutionary design of combinational circuits. In contrast to standard point mutation modifying the values of the mutated genes randomly, the proposed operator uses semantics to determine the best value for each mutated gene. Compared to the common CGP and its variants, the proposed method converges on common Boolean benchmarks substantially faster while keeping the phenotype size relatively small. The successfully evolved instances presented in this paper include 10-bit parity, 10 + 10-bit adder and 5(times)5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"29 7","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496051","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