標(biāo)題: Titlebook: Genetic Programming Theory and Practice II; Una-May O’Reilly,Tina Yu,Bill Worzel Book 2005 Springer-Verlag US 2005 Algorithms.Automat.algo [打印本頁(yè)] 作者: 女孩 時(shí)間: 2025-3-21 19:27
書(shū)目名稱Genetic Programming Theory and Practice II影響因子(影響力)
書(shū)目名稱Genetic Programming Theory and Practice II影響因子(影響力)學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice II網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Genetic Programming Theory and Practice II網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice II被引頻次
書(shū)目名稱Genetic Programming Theory and Practice II被引頻次學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice II年度引用
書(shū)目名稱Genetic Programming Theory and Practice II年度引用學(xué)科排名
書(shū)目名稱Genetic Programming Theory and Practice II讀者反饋
書(shū)目名稱Genetic Programming Theory and Practice II讀者反饋學(xué)科排名
作者: 名次后綴 時(shí)間: 2025-3-21 22:18
Using Genetic Programming in Industrial Statistical Model Building,odology for using Genetic Programming in statistical modeling of designed and undesigned data is described and illustrated with successful industrial applications. As a result of the synergistic efforts, the building technique has been improved and the model development cost and time can be signific作者: AGONY 時(shí)間: 2025-3-22 00:56 作者: tic-douloureux 時(shí)間: 2025-3-22 08:06
Considering the Roles of Structure in Problem Solving by Computer,ore difficult than others for genetic programming to solve. This chapter subsequently summarizes my group’s current theoretical work at the University of Michigan and extends the implications of that work to real-world problem solving.作者: 種植,培養(yǎng) 時(shí)間: 2025-3-22 10:56 作者: Sleep-Paralysis 時(shí)間: 2025-3-22 16:54
Favourable Biasing of Function Sets Using Run Transferable Libraries,ith standard programming libraries as they provide a suite of functions that can not only be used across several runs on a particular problem, but also to aid in the scaling of a system to more difficult instances of a problem. This is achieved by . a library on a relatively simple instance of a pro作者: Sleep-Paralysis 時(shí)間: 2025-3-22 20:43 作者: 障礙 時(shí)間: 2025-3-22 23:53 作者: fender 時(shí)間: 2025-3-23 02:12 作者: 復(fù)習(xí) 時(shí)間: 2025-3-23 08:05
ACGP: Adaptable Constrained Genetic Programming,the user is often forced to provide a large set, thus enlarging the search space — often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming method作者: 迅速成長(zhǎng) 時(shí)間: 2025-3-23 11:25
Using Genetic Programming to Search for Supply Chain Reordering Policies,line their method that integrates a supply chain simulation with genetic programming. The simulation is used to score the population members for the evolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member re作者: 自愛(ài) 時(shí)間: 2025-3-23 16:20
Cartesian Genetic Programming and the Post Docking Filtering Problem,ds, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of C作者: JOG 時(shí)間: 2025-3-23 21:58
Listening to Data: Tuning a Genetic Programming System, into the data itself. This is discussed using examples from classification problems in molecular biology and the results and “rules of thumb” developed to tune the GP system are reviewed in light of current GP theory.作者: 不朽中國(guó) 時(shí)間: 2025-3-24 00:08 作者: 充足 時(shí)間: 2025-3-24 05:38 作者: AV-node 時(shí)間: 2025-3-24 07:38 作者: Aspiration 時(shí)間: 2025-3-24 12:26 作者: V切開(kāi) 時(shí)間: 2025-3-24 17:30
Genetic Programming of an Algorithmic Chemistry,We introduce a new method of execution for GP-evolved programs consisting of register machine instructions. It is shown that this method can be considered as an artificial chemistry. It lends itself well to distributed and parallel computing schemes in which synchronization and coordination are not an issue.作者: CHART 時(shí)間: 2025-3-24 19:19
Incident Detection on Highways,This chapter discusses the development of the Low-occupancy INcident Detection Algorithm (LINDA) that detects night-time motorway incidents. LINDA is undergoing testing on live data and deployment on the M5, M6 and other motorways in the United Kingdom. It was developed by the authors using Genetic Programming.作者: 有發(fā)明天才 時(shí)間: 2025-3-25 00:59
https://doi.org/10.1007/b101112Algorithms; Automat; algorithm; computer; genetic programming; learning; machine learning; programming; algo作者: Agility 時(shí)間: 2025-3-25 03:43 作者: 弄臟 時(shí)間: 2025-3-25 10:04
978-1-4419-3589-2Springer-Verlag US 2005作者: 脫毛 時(shí)間: 2025-3-25 12:42
Genetic Programming Theory and Practice II978-0-387-23254-6Series ISSN 1566-7863 作者: graphy 時(shí)間: 2025-3-25 19:38
Understanding Critical Thinkingore difficult than others for genetic programming to solve. This chapter subsequently summarizes my group’s current theoretical work at the University of Michigan and extends the implications of that work to real-world problem solving.作者: extinguish 時(shí)間: 2025-3-25 20:51 作者: 威脅你 時(shí)間: 2025-3-26 02:36
Data Science and Digital Business into the data itself. This is discussed using examples from classification problems in molecular biology and the results and “rules of thumb” developed to tune the GP system are reviewed in light of current GP theory.作者: 不發(fā)音 時(shí)間: 2025-3-26 06:10
Considering the Roles of Structure in Problem Solving by Computer,ore difficult than others for genetic programming to solve. This chapter subsequently summarizes my group’s current theoretical work at the University of Michigan and extends the implications of that work to real-world problem solving.作者: 俗艷 時(shí)間: 2025-3-26 09:18
Does Genetic Programming Inherently Adopt Structured Design Techniques?,tiveness of these techniques, we define a design as an evolutionary frozen root node. We show that GP design converges quickly based primarily on the best individual in the initial random population. This leads to speculation of several mechanisms that could be used to allow basic GP techniques to better incorporate top-down design principles.作者: 喚起 時(shí)間: 2025-3-26 13:42 作者: garrulous 時(shí)間: 2025-3-26 18:32 作者: Expostulate 時(shí)間: 2025-3-26 21:15 作者: Pert敏捷 時(shí)間: 2025-3-27 04:08
Unüberwachtes maschinelles Lernenin (Goldberg et al., 1992), it considers building block decision-making as a key facet. The analysis yields a GP-unique relationship because it has to account for bloat and for the fact that GP solutions often use subsolutions multiple times. The population-sizing relationship depends upon tree size作者: hemorrhage 時(shí)間: 2025-3-27 09:00 作者: 輕快來(lái)事 時(shí)間: 2025-3-27 09:43 作者: 賞錢 時(shí)間: 2025-3-27 16:35 作者: strdulate 時(shí)間: 2025-3-27 21:45
https://doi.org/10.1007/978-1-4842-3597-3. In particular, genetic programming has automatically synthesized structures that infringe, improve upon, or duplicate the functionality of 21 previously patented inventions (including six 21.-century patented analog electrical circuits) and has also generated two patentable new inventions (control作者: 正論 時(shí)間: 2025-3-28 00:37
Classification Using Decision Trees,tness of the system. This chapter proposes that robust design should start from the conceptual design stage and genetic programming-based open-ended topology search can be used for automated synthesis of robust systems. Combined with a bond graph-based dynamic system synthesis methodology, an improv作者: 他一致 時(shí)間: 2025-3-28 02:53
https://doi.org/10.1007/978-1-4842-6405-8tiveness of these techniques, we define a design as an evolutionary frozen root node. We show that GP design converges quickly based primarily on the best individual in the initial random population. This leads to speculation of several mechanisms that could be used to allow basic GP techniques to b作者: exorbitant 時(shí)間: 2025-3-28 09:49
https://doi.org/10.1007/978-1-4842-2614-8the user is often forced to provide a large set, thus enlarging the search space — often resulting in reducing the search efficiency. Moreover, based on heuristics, syntactic constraints, or data typing, a given subtree may be undesired or invalid in a given context. Typed Genetic Programming method作者: GRILL 時(shí)間: 2025-3-28 12:55
Data Science and Analytics for SMEsline their method that integrates a supply chain simulation with genetic programming. The simulation is used to score the population members for the evolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member re作者: forbid 時(shí)間: 2025-3-28 17:48
Data Science and Big Data Computingds, these technologies are less successful at ranking true hits correctly by binding free energy. This chapter presents the automated removal of false positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of C作者: Flagging 時(shí)間: 2025-3-28 22:02 作者: Inflated 時(shí)間: 2025-3-29 02:32 作者: Alopecia-Areata 時(shí)間: 2025-3-29 05:19
Data Science for Economics and Financeurrent methods of designing and optimizing antennas by hand are time and labor intensive, limit complexity, and require significant expertise and experience. Evolutionary design techniques can overcome these limitations by searching the design space and automatically finding effective solutions that作者: pericardium 時(shí)間: 2025-3-29 09:50 作者: 使長(zhǎng)胖 時(shí)間: 2025-3-29 12:33 作者: ascend 時(shí)間: 2025-3-29 16:19
Usman Qamar,Muhammad Summair Razablem before applying it to the more difficult one..The chapter examines the dynamics of the library internals, and how functions compete for dominance of the library. We demonstrate that the libraries tend to converge on a small number of functions, and identify methods to test how well a library is likely to be able to scale.作者: absorbed 時(shí)間: 2025-3-29 23:34
Classification Using Decision Trees,ed sustainable genetic programming technique - quick hierarchical fair competition (QHFC)- is used to evolve robust high-pass analog filters. It is shown that topological innovation by genetic programming can be used to improve the robustness of evolved design solutions with respect to both parameter perturbations and topology faults.作者: 蒸發(fā) 時(shí)間: 2025-3-30 00:36 作者: Prophylaxis 時(shí)間: 2025-3-30 06:52
Using Genetic Programming in Industrial Statistical Model Building,antly reduced. In case of designed data Genetic Programming reduced costs by suggesting transformations as an alternative to doing additional experimentation. In case of undesigned data Genetic Programming was instrumental in reducing the model building costs by providing alternative models for consideration.作者: 高度贊揚(yáng) 時(shí)間: 2025-3-30 08:19
Favourable Biasing of Function Sets Using Run Transferable Libraries,blem before applying it to the more difficult one..The chapter examines the dynamics of the library internals, and how functions compete for dominance of the library. We demonstrate that the libraries tend to converge on a small number of functions, and identify methods to test how well a library is likely to be able to scale.作者: RAG 時(shí)間: 2025-3-30 15:36
Topological Synthesis of Robust Dynamic Systems by Sustainable Genetic Programming,ed sustainable genetic programming technique - quick hierarchical fair competition (QHFC)- is used to evolve robust high-pass analog filters. It is shown that topological innovation by genetic programming can be used to improve the robustness of evolved design solutions with respect to both parameter perturbations and topology faults.作者: Musculoskeletal 時(shí)間: 2025-3-30 19:26
Unüberwachtes maschinelles Lernenopulation sizing for three model GP problems named ORDER, ON-OFF and LOUD. These problems exhibit bloat to differing extents and differ in whether their solutions require the use of a building block multiple times.作者: Extort 時(shí)間: 2025-3-30 22:45 作者: overbearing 時(shí)間: 2025-3-31 04:31
Book 2005e Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of this workshop series is to promote the exchange of research results and ideas between those who focus on Genetic Programming (GP) theory and those who focus on the application of GP to various re- world 作者: Harridan 時(shí)間: 2025-3-31 06:28
Lessons Learned Using Genetic Programming in a Stock Picking Context,and in gaining acceptance of the technique by a skeptical audience. We discuss in some detail the construction of the fitness function, the genetic programming system’s parameterization (including data selection and internal function choice), and the interpretation and modification of the generated programs for eventual implementation.作者: 富足女人 時(shí)間: 2025-3-31 12:17 作者: 恃強(qiáng)凌弱 時(shí)間: 2025-3-31 16:50
Cartesian Genetic Programming and the Post Docking Filtering Problem, positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of CGP for this problem and confirm the absence of bloat and the usefulness of neutral drift.作者: MIR 時(shí)間: 2025-3-31 19:53
1566-7863 enetic Programming.Includes supplementary material: The work described in this book was first presented at the Second Workshop on Genetic Programming, Theory and Practice, organized by the Center for the Study of Complex Systems at the University of Michigan, Ann Arbor, 13-15 May 2004. The goal of t作者: 法官 時(shí)間: 2025-3-31 23:28 作者: IDEAS 時(shí)間: 2025-4-1 04:11
Data Science and Analytics for SMEsvolutionary algorithm which is, in turn, used to search for members that might perform better on the simulation. The fitness of a population member reflects its relative performance in the simulation. This paper investigates both the effectiveness of this method and the parameter settings that make it more or less effective.作者: Glucose 時(shí)間: 2025-4-1 09:26
Data Science and Big Data Computing positives from virtual hit sets, by evolving a post docking filter using Cartesian Genetic Programming(CGP). We also investigate characteristics of CGP for this problem and confirm the absence of bloat and the usefulness of neutral drift.作者: 預(yù)測(cè) 時(shí)間: 2025-4-1 10:56 作者: 思想流動(dòng) 時(shí)間: 2025-4-1 14:58
https://doi.org/10.1007/978-1-4842-2614-8 particular problem, by extracting and utilizing such heuristics. Even though many specific techniques can be implemented in the methodology, in this paper we utilize information on local first-order (parent-child) distributions of the functions and terminals. The heuristics are extracted from the p
