Pub Date : 2022-01-05DOI: 10.1007/s10710-022-09429-9
J. Mccormack, Camilo Cruz Gambardella
{"title":"Complexity and aesthetics in generative and evolutionary art","authors":"J. Mccormack, Camilo Cruz Gambardella","doi":"10.1007/s10710-022-09429-9","DOIUrl":"https://doi.org/10.1007/s10710-022-09429-9","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"23 1","pages":"535 - 556"},"PeriodicalIF":2.6,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49050797","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}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-02056-8
{"title":"Genetic Programming: 25th European Conference, EuroGP 2022, Held as Part of EvoStar 2022, Madrid, Spain, April 20–22, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-02056-8","DOIUrl":"https://doi.org/10.1007/978-3-031-02056-8","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"11 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87133014","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}
Pub Date : 2021-11-25DOI: 10.1007/s10710-021-09425-5
Dominik Sobania, J. Schmitt, H. Köstler, Franz Rothlauf
{"title":"Genetic programming for iterative numerical methods","authors":"Dominik Sobania, J. Schmitt, H. Köstler, Franz Rothlauf","doi":"10.1007/s10710-021-09425-5","DOIUrl":"https://doi.org/10.1007/s10710-021-09425-5","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"23 1","pages":"253 - 278"},"PeriodicalIF":2.6,"publicationDate":"2021-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43930738","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}
Pub Date : 2021-11-18DOI: 10.1007/s10710-021-09424-6
Sergio Contador, J. Colmenar, O. Garnica, J. M. Velasco, J. Hidalgo
{"title":"Blood glucose prediction using multi-objective grammatical evolution: analysis of the “agnostic” and “what-if” scenarios","authors":"Sergio Contador, J. Colmenar, O. Garnica, J. M. Velasco, J. Hidalgo","doi":"10.1007/s10710-021-09424-6","DOIUrl":"https://doi.org/10.1007/s10710-021-09424-6","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"23 1","pages":"161 - 192"},"PeriodicalIF":2.6,"publicationDate":"2021-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43483386","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}
Pub Date : 2021-10-26DOI: 10.1007/s10710-021-09422-8
Grace Buttler
{"title":"Artificial intelligence for fashion, Leanne Luce, Apress 2019, ISBN 978-1-4842-3930-8 how AI is revolutionizing the fashion industry","authors":"Grace Buttler","doi":"10.1007/s10710-021-09422-8","DOIUrl":"https://doi.org/10.1007/s10710-021-09422-8","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"28 1","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517139","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}
Pub Date : 2021-10-25DOI: 10.1007/s10710-021-09420-w
Walid Magdy
{"title":"Robert Elliott Smith: Rage Inside the Machine—the prejudice of algorithms, and how to stop the internet making bigots of us all","authors":"Walid Magdy","doi":"10.1007/s10710-021-09420-w","DOIUrl":"https://doi.org/10.1007/s10710-021-09420-w","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"29 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496056","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}
Pub Date : 2021-10-21DOI: 10.1007/s10710-021-09423-7
Campos, Marcelino, Sempere, José M.
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.
{"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":"<p>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.</p>","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}
Pub Date : 2021-10-05DOI: 10.1007/s10710-021-09419-3
Stefano Ruberto, Valerio Terragni, Jason H. Moore
{"title":"A semantic genetic programming framework based on dynamic targets","authors":"Stefano Ruberto, Valerio Terragni, Jason H. Moore","doi":"10.1007/s10710-021-09419-3","DOIUrl":"https://doi.org/10.1007/s10710-021-09419-3","url":null,"abstract":"","PeriodicalId":50424,"journal":{"name":"Genetic Programming and Evolvable Machines","volume":"22 1","pages":"463 - 493"},"PeriodicalIF":2.6,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46475728","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}
Pub Date : 2021-10-02DOI: 10.1007/s10710-021-09416-6
Hodan, David, Mrazek, Vojtech, Vasicek, Zdenek
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.
{"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":"<p>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<span>(times)</span>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 (<span>(mathrm {SOMO}^k)</span>) 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<span>(times)</span>5-bit multiplier. The most complex circuits were evolved in less than one hour with a single-thread implementation running on a common CPU.</p>","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}
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