標(biāo)題: Titlebook: Genetic Programming Theory and Practice XII; Rick Riolo,William P. Worzel,Mark Kotanchek Book 2015 Springer International Publishing Switz [打印本頁(yè)] 作者: fasten 時(shí)間: 2025-3-21 16:42
書(shū)目名稱Genetic Programming Theory and Practice XII影響因子(影響力)
書(shū)目名稱Genetic Programming Theory and Practice XII影響因子(影響力)學(xué)科排名
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書(shū)目名稱Genetic Programming Theory and Practice XII網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice XII被引頻次
書(shū)目名稱Genetic Programming Theory and Practice XII被引頻次學(xué)科排名
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書(shū)目名稱Genetic Programming Theory and Practice XII年度引用學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice XII讀者反饋
書(shū)目名稱Genetic Programming Theory and Practice XII讀者反饋學(xué)科排名
作者: 包庇 時(shí)間: 2025-3-21 23:09
Gunter Schlageter,Wolffried Stuckytranscription factor binding and histone modifications, to identify novel regulatory DNA elements in the genomes, and to predict gene functions. We also discuss the advantages and limitations of genetic programming in analyzing and processing biological data.作者: 其他 時(shí)間: 2025-3-22 03:31
https://doi.org/10.1007/978-3-658-38585-9stems. This paper describes the implementation of this system, gives some examples of successful applications constructed using the SKGP and describes future directions that may offer a more powerful GP system capable of producing more complex programs.作者: APEX 時(shí)間: 2025-3-22 07:20
P.-F. Kuhrt,R. Giesecke,V. Maurerc approaches that consistently show up in the HUMIE winners. We believe that this analysis may lead to interesting insights regarding prospects and strategies for producing further human competitive results.作者: 無(wú)能力 時(shí)間: 2025-3-22 10:59 作者: 斗志 時(shí)間: 2025-3-22 12:58
SKGP: The Way of the Combinator,stems. This paper describes the implementation of this system, gives some examples of successful applications constructed using the SKGP and describes future directions that may offer a more powerful GP system capable of producing more complex programs.作者: 斗志 時(shí)間: 2025-3-22 17:42
,Analyzing a Decade of Human-Competitive (“HUMIE”) Winners: What Can We Learn?,c approaches that consistently show up in the HUMIE winners. We believe that this analysis may lead to interesting insights regarding prospects and strategies for producing further human competitive results.作者: 精密 時(shí)間: 2025-3-22 23:06
Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System,ons and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller solutions. We also consider the effect of pool size and the choice of fitness function on convergence and total computation.作者: 惹人反感 時(shí)間: 2025-3-23 03:51 作者: 拾落穗 時(shí)間: 2025-3-23 09:31 作者: 完成才會(huì)征服 時(shí)間: 2025-3-23 11:55
Book 2015ion, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.作者: Negotiate 時(shí)間: 2025-3-23 16:24 作者: 無(wú)彈性 時(shí)間: 2025-3-23 19:20 作者: 解決 時(shí)間: 2025-3-24 00:12
Rick Riolo,William P. Worzel,Mark KotanchekProvides papers describing cutting-edge work on genetic programming theory, applications of GP and theory.Offers large-scale, real-world applications of GP to a variety of problem domains, from financ作者: 威脅你 時(shí)間: 2025-3-24 06:10
Gunter Schlageter,Wolffried Stuckyhese large-scale datasets make it possible and necessary to implement machine learning techniques for mining biological insights. In this chapter, we describe several examples to show how machine learning approaches are used to elucidate the mechanism of transcriptional regulation mediated by transc作者: 思想上升 時(shí)間: 2025-3-24 08:40
Programmentwicklung im dBASE-Formatuence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. T作者: Diaphragm 時(shí)間: 2025-3-24 11:19
Patricia Deflorin,Maike Scherrer,Toni W?flerrograms. Here we extend the biological analogy to incorporate epigenetic regulation through both learning and evolution. We begin the chapter with a discussion of Darwinian, Lamarckian, and Baldwinian approaches to evolutionary computation and describe how recent findings in biology differ conceptua作者: genesis 時(shí)間: 2025-3-24 14:50
https://doi.org/10.1007/978-3-658-38585-9lity of GP depends on the representation of programs in the population and how to handle illegal or type incoherent expressions that arise from crossover and mutation within a population of programs. The SKGP is a GP system that uses graphs of . to represent functions and a strong type system to inf作者: 繼承人 時(shí)間: 2025-3-24 19:46
https://doi.org/10.1007/978-3-658-23240-5od is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances作者: 帶來(lái)的感覺(jué) 時(shí)間: 2025-3-25 00:44
https://doi.org/10.1007/978-3-658-14445-6window defines the portion of the data visible to the algorithm during training and is moved over the data. The window is moved regularly based on the generations or on the current selection pressure when using offspring selection. The sliding window technique has the effect that population has to a作者: corporate 時(shí)間: 2025-3-25 03:28
Physikalische Eigenschaften von Gasen,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: 簡(jiǎn)略 時(shí)間: 2025-3-25 09:19
https://doi.org/10.1007/978-3-658-11655-2ically for any regression problem. With this knowledge in mind, the objective of this chapter is to discuss two Genetic Programming (GP) models aimed at finding pairs of optimally aligned individuals. The first one of these models, already introduced in a previous publication, is ESAGP-1. The second作者: Middle-Ear 時(shí)間: 2025-3-25 14:00
P.-F. Kuhrt,R. Giesecke,V. Maurers that can be solved by EC-based approaches. The HUMIES awards at the Genetic and Evolutionary Computation Conference are designed to recognize work that has not just solved some problem via techniques from evolutionary computation, but has produced a solution that is demonstrably human-competitive.作者: 極大痛苦 時(shí)間: 2025-3-25 19:37
Physikalische und technische Grundlagen,iplexer function, both on a single machine using a full-fitness evaluation method, as well as using distributed, age-layered, partial-fitness evaluations and a Pitts-style representation. We study the impact of age-layering and show how the system scales with distribution and tends towards smaller s作者: 不發(fā)音 時(shí)間: 2025-3-25 22:26
978-3-319-38376-7Springer International Publishing Switzerland 2015作者: Erythropoietin 時(shí)間: 2025-3-26 01:36 作者: IRATE 時(shí)間: 2025-3-26 06:08
https://doi.org/10.1007/978-3-319-16030-6Artificial evolution; Evolution of models; Feature selection; Genetic programming; Genetic programming a作者: 暫時(shí)別動(dòng) 時(shí)間: 2025-3-26 12:28 作者: 母豬 時(shí)間: 2025-3-26 14:39
,Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based As of genetic variation from lists of mathematical functions using a form of genetic programming called computational evolution. A key feature of the system is the ability to utilize pre-processed expert knowledge giving it the ability to explore model space much as a human would. We describe this sy作者: PLIC 時(shí)間: 2025-3-26 18:29 作者: Inflated 時(shí)間: 2025-3-27 00:42
Sequential Symbolic Regression with Genetic Programming,cantly outperforms SGP and presents no statistical difference from GP. More importantly, they show the potential of the proposed approach: an effective way of applying geometric semantic operators to combine different (partial) solutions, and at the same time, avoiding the exponential growth problem作者: 憲法沒(méi)有 時(shí)間: 2025-3-27 02:46
Extremely Accurate Symbolic Regression for Large Feature Problems,etail. This algorithm was extremely accurate, on a single processor, for up to 25 features (columns); and, a cloud configuration was used to extend the extreme accuracy up to as many as 100 features..While the previous algorithm’s extreme accuracy for deep problems with a small number of features (2作者: paroxysm 時(shí)間: 2025-3-27 09:21
How to Exploit Alignment in the Error Space: Two Different GP Models,andard GP and geometric semantic GP on two complex real-life applications. At the same time, a preliminary set of results obtained on a set of symbolic regression benchmarks indicate that POGP, although rather new and still in need of improvement, is a very promising model, that deserves future deve作者: 外科醫(yī)生 時(shí)間: 2025-3-27 11:05 作者: Nebulous 時(shí)間: 2025-3-27 16:11 作者: Stagger 時(shí)間: 2025-3-27 18:38
https://doi.org/10.1007/978-3-658-23240-5cantly outperforms SGP and presents no statistical difference from GP. More importantly, they show the potential of the proposed approach: an effective way of applying geometric semantic operators to combine different (partial) solutions, and at the same time, avoiding the exponential growth problem作者: 較早 時(shí)間: 2025-3-28 00:06 作者: Suppository 時(shí)間: 2025-3-28 02:19
https://doi.org/10.1007/978-3-658-11655-2andard GP and geometric semantic GP on two complex real-life applications. At the same time, a preliminary set of results obtained on a set of symbolic regression benchmarks indicate that POGP, although rather new and still in need of improvement, is a very promising model, that deserves future deve作者: 除草劑 時(shí)間: 2025-3-28 09:32 作者: 角斗士 時(shí)間: 2025-3-28 10:40
,Identification of Novel Genetic Models of Glaucoma Using the “EMERGENT” Genetic Programming-Based Auence risk in the context of our local ecology. The complexity of the genotype to phenotype mapping relationship for common diseases like POAG necessitates analytical approaches that move beyond parametric statistical methods such as logistic regression that assume a particular mathematical model. T作者: geometrician 時(shí)間: 2025-3-28 16:56
Inheritable Epigenetics in Genetic Programming,rograms. Here we extend the biological analogy to incorporate epigenetic regulation through both learning and evolution. We begin the chapter with a discussion of Darwinian, Lamarckian, and Baldwinian approaches to evolutionary computation and describe how recent findings in biology differ conceptua作者: Cubicle 時(shí)間: 2025-3-28 21:24 作者: 取消 時(shí)間: 2025-3-29 01:58
Sequential Symbolic Regression with Genetic Programming,od is inspired by the sequential covering strategy from machine learning, but instead of sequentially reducing the size of the problem being solved, it sequentially transforms the original problem into potentially simpler problems. This transformation is performed according to the semantic distances作者: Multiple 時(shí)間: 2025-3-29 05:19
Sliding Window Symbolic Regression for Detecting Changes of System Dynamics,window defines the portion of the data visible to the algorithm during training and is moved over the data. The window is moved regularly based on the generations or on the current selection pressure when using offspring selection. The sliding window technique has the effect that population has to a
