標(biāo)題: Titlebook: Genetic Programming Theory and Practice XIII; Rick Riolo,W.P.‘Worzel,Arthur Kordon Book 2016 Springer International Publishing Switzerland [打印本頁] 作者: 可入到 時間: 2025-3-21 19:28
書目名稱Genetic Programming Theory and Practice XIII影響因子(影響力)
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書目名稱Genetic Programming Theory and Practice XIII網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genetic Programming Theory and Practice XIII被引頻次
書目名稱Genetic Programming Theory and Practice XIII被引頻次學(xué)科排名
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書目名稱Genetic Programming Theory and Practice XIII年度引用學(xué)科排名
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書目名稱Genetic Programming Theory and Practice XIII讀者反饋學(xué)科排名
作者: 沒有貧窮 時間: 2025-3-21 22:49 作者: Dictation 時間: 2025-3-22 02:17
GP As If You Meant It: An Exercise for Mindful Practice,g software developers, it’s an exercise designed to help participants hone their skills through mindful practice. Its intent is to surface certain unquestioned habits common in our field: to make the participants painfully aware of the tacit . for certain GP algorithm design decisions they may other作者: Hemodialysis 時間: 2025-3-22 06:50 作者: Malleable 時間: 2025-3-22 08:54
Highly Accurate Symbolic Regression with Noisy Training Data,ced commercial packages, has become an issue for early adopters. Users expect to have the correct formula returned, especially in cases with zero noise and only one basis function with minimally complex grammar depth..At a minimum, users expect the response surface of the SR tool to be easily unders作者: BLA 時間: 2025-3-22 16:57 作者: BLA 時間: 2025-3-22 19:39
The Evolution of Everything (EvE) and Genetic Programming,essors are sensors that continuously collect data. By 2020, it is projected that?there may be more than 20 billion (1000 million) devices connected to the Internet. Collectively these devices are called the Internet of Things (IoT) or the Internet of Everything (IoE). The sheer volume of the data th作者: 螢火蟲 時間: 2025-3-22 22:09
Lexicase Selection for Program Synthesis: A Diversity Analysis,rmance on test cases, considered in random order. When used as the parent selection method in genetic programming, lexicase selection has been shown to provide significant improvements in problem-solving power. In this chapter we investigate the reasons for the success of lexicase selection, focusin作者: 腐蝕 時間: 2025-3-23 02:47
Behavioral Program Synthesis: Insights and Prospects,-output mapping. The number of passed tests, or the total error in continuous domains, is a natural objective measure of a program’s performance and a common yardstick when experimentally comparing algorithms. In GP, it is also by default used to . the evolutionary search process. An assumption that作者: 子女 時間: 2025-3-23 07:48
Using Graph Databases to Explore the Dynamics of Genetic Programming Runs,ften obscure or completely mask the profusion of specific selections,?crossovers, and mutations that are ultimately responsible for the aggregate behaviors we’re interested in. In this chapter we take a different approach and use the Neo4j graph database system to record and analyze the entire genea作者: 警告 時間: 2025-3-23 10:12 作者: canonical 時間: 2025-3-23 15:41 作者: Altitude 時間: 2025-3-23 19:43 作者: Amnesty 時間: 2025-3-24 00:51 作者: 萬神殿 時間: 2025-3-24 03:00
Rick Riolo,W.P.‘Worzel,Arthur KordonProvides papers describing cutting-edge work on the theory and applications of genetic programming (GP).Offers large-scale, real-world applications of GP to a variety of problem domains, including fin作者: paroxysm 時間: 2025-3-24 06:49
Kristof Obermann,Martin Hornefferbolic regression armed with the power to select right variables and variable combinations, build robust trustable predictions and guide experimentation has undoubtedly earned its place in industrial process optimization, business forecasting, product design and now complex systems modeling and policy making.作者: 入伍儀式 時間: 2025-3-24 13:46
Prime-Time: Symbolic Regression Takes Its Place in the Real World,bolic regression armed with the power to select right variables and variable combinations, build robust trustable predictions and guide experimentation has undoubtedly earned its place in industrial process optimization, business forecasting, product design and now complex systems modeling and policy making.作者: 攝取 時間: 2025-3-24 18:03
978-3-319-81706-4Springer International Publishing Switzerland 2016作者: Insulin 時間: 2025-3-24 22:47 作者: 躲債 時間: 2025-3-25 02:05 作者: noxious 時間: 2025-3-25 06:51
https://doi.org/10.1007/978-3-322-85477-3instances’ class. A classification tool has to discover how to separate classes based on features, but the discovery of useful knowledge may be hampered by inadequate or insufficient features. Pre-processing steps for the automatic construction of new high-level features proposed to discover hidden 作者: 出價 時間: 2025-3-25 11:18 作者: archetype 時間: 2025-3-25 15:01
Netzdienste der Deutschen Telekom,segments are mutually exclusive, and training takes place only on one segment. This system is well suited to run in concert with the EC-Star distributed Evolutionary system, cross-validating solution candidates during a run. The system is tested with different numbers of validation segments using a 作者: atopic-rhinitis 時間: 2025-3-25 17:56 作者: ureter 時間: 2025-3-25 23:45
https://doi.org/10.1007/978-3-663-08344-3d of Data Science. Data Science refers to the practice of extracting knowledge from data, often Big Data, to glean insights useful for predicting business, political or societal outcomes. Data Science tools are important to the practice as they allow Data Scientists to be productive and accurate. GP作者: STANT 時間: 2025-3-26 00:39
https://doi.org/10.1007/978-3-322-90820-9essors are sensors that continuously collect data. By 2020, it is projected that?there may be more than 20 billion (1000 million) devices connected to the Internet. Collectively these devices are called the Internet of Things (IoT) or the Internet of Everything (IoE). The sheer volume of the data th作者: grounded 時間: 2025-3-26 07:05
https://doi.org/10.1007/3-540-29332-9rmance on test cases, considered in random order. When used as the parent selection method in genetic programming, lexicase selection has been shown to provide significant improvements in problem-solving power. In this chapter we investigate the reasons for the success of lexicase selection, focusin作者: 和音 時間: 2025-3-26 08:40 作者: hangdog 時間: 2025-3-26 13:26
Wissenschaftliche Publikationen,ften obscure or completely mask the profusion of specific selections,?crossovers, and mutations that are ultimately responsible for the aggregate behaviors we’re interested in. In this chapter we take a different approach and use the Neo4j graph database system to record and analyze the entire genea作者: 馬賽克 時間: 2025-3-26 19:57
https://doi.org/10.1007/978-3-642-61828-4ble managers to combine these utility values in different ways to predict the market share of a product with a new configuration of features. Researchers assess the accuracy of these choice models by measuring the extent to which the summed utilities can predict actual market shares when respondents作者: 樹膠 時間: 2025-3-27 00:23 作者: Individual 時間: 2025-3-27 04:06 作者: Intercept 時間: 2025-3-27 07:41 作者: 不連貫 時間: 2025-3-27 11:13 作者: 高深莫測 時間: 2025-3-27 13:51
Wissenschaftliche Publikationen,e broadly, we illustrate the value of recording and analyzing this level of detail, both as a means of understanding the dynamics of particular runs, and as a way of generating questions and ideas for subsequent, broader study.作者: 厚臉皮 時間: 2025-3-27 20:56
Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming,e on several benchmark problems. As a result of the multi-objective approach the appropriate model length and the functions included in the models are automatically determined without the necessity to specify them a-priori.作者: 擔(dān)心 時間: 2025-3-28 00:29
Using Graph Databases to Explore the Dynamics of Genetic Programming Runs,e broadly, we illustrate the value of recording and analyzing this level of detail, both as a means of understanding the dynamics of particular runs, and as a way of generating questions and ideas for subsequent, broader study.作者: Vsd168 時間: 2025-3-28 03:54
Book 2016retical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synth作者: groggy 時間: 2025-3-28 09:10
1932-0167 cations of GP to a variety of problem domains, including finThese contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of th作者: BAIL 時間: 2025-3-28 12:39
Netzdienste der Deutschen Telekom,ed Evolutionary system, cross-validating solution candidates during a run. The system is tested with different numbers of validation segments using a real-world problem of classifying ICU blood-pressure time series.作者: 深陷 時間: 2025-3-28 17:19 作者: 現(xiàn)暈光 時間: 2025-3-28 20:04
https://doi.org/10.1007/3-540-29332-9d selection based on implicit fitness sharing. We conclude that lexicase selection does indeed produce more diverse populations, which helps to explain the utility of lexicase selection for program synthesis.作者: 流浪 時間: 2025-3-28 23:40
,Der SAP-Ansatz für die Datenmodellierung,her types of GP. We test three variants of this new approach on a large set of benchmark problems from several different sources, and observe their competitiveness against the most successful state-of-the-art classifiers like Random Forests, Random Subspaces and Multilayer Perceptron.作者: 脖子 時間: 2025-3-29 06:57 作者: 孤僻 時間: 2025-3-29 07:56 作者: Agnosia 時間: 2025-3-29 12:46 作者: 冥界三河 時間: 2025-3-29 17:13
nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star,ed Evolutionary system, cross-validating solution candidates during a run. The system is tested with different numbers of validation segments using a real-world problem of classifying ICU blood-pressure time series.作者: 輪流 時間: 2025-3-29 22:58
https://doi.org/10.1007/978-3-322-85478-0kit. The other “player” is the automated GP System itself, which adds to a growing population of solutions by applying the search operators and evaluation functions specified by the User player. The User’s goal is to convince the System to produce “good enough” answers to a target supervised learnin作者: Insulin 時間: 2025-3-30 03:57
,DFü in Turbo-Pascal (?Verbindung“), detail in these two previous papers. This algorithm is extremely accurate, in reasonable time on a single processor, for from 25 up to 3000 features (columns)..Extensive statistically correct, out of sample training and testing, demonstrated the extreme accuracy algorithm’s advantages over a previo作者: 無效 時間: 2025-3-30 04:20 作者: URN 時間: 2025-3-30 10:09
https://doi.org/10.1007/978-3-322-90820-9ning algorithms. The confluence of unimaginable streams of real-world data and emergent behaviors may give rise to the question of whether the evolution of intelligence in the natural world can be recreated using evolutionary tools.作者: synovium 時間: 2025-3-30 15:03
