標(biāo)題: Titlebook: Genetic Programming; 17th European Confer Miguel Nicolau,Krzysztof Krawiec,Kevin Sim Conference proceedings 2014 Springer-Verlag Berlin Hei [打印本頁] 作者: Asphyxia 時(shí)間: 2025-3-21 18:28
書目名稱Genetic Programming影響因子(影響力)
書目名稱Genetic Programming影響因子(影響力)學(xué)科排名
書目名稱Genetic Programming網(wǎng)絡(luò)公開度
書目名稱Genetic Programming網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Genetic Programming被引頻次
書目名稱Genetic Programming被引頻次學(xué)科排名
書目名稱Genetic Programming年度引用
書目名稱Genetic Programming年度引用學(xué)科排名
書目名稱Genetic Programming讀者反饋
書目名稱Genetic Programming讀者反饋學(xué)科排名
作者: Anticonvulsants 時(shí)間: 2025-3-21 21:29 作者: 小官 時(shí)間: 2025-3-22 04:25 作者: forestry 時(shí)間: 2025-3-22 06:44
Behavioral Search Drivers for Genetic Programingidely adopted stance, there is no evidence that this quality measure is the best choice; alternative . may exist that make search more effective. This study proposes and investigates a new family of ., which inspect not only final program output, but also program behavior meant as the partial results it arrives at while executed.作者: 證明無罪 時(shí)間: 2025-3-22 09:09
Cartesian Genetic Programming: Why No Bloat? CGP does not suffer from bloat. It has also been shown for CGP that using a very large number of nodes considerably increases the effectiveness of the search. This paper also proposes a new explanation as to why this may be the case.作者: 搖晃 時(shí)間: 2025-3-22 14:10
https://doi.org/10.1007/978-3-662-63562-9indicate that this is the case in two benchmark problems, in accordance with results for Flat-OE. In conclusion, NEAT provides a worthwhile strategy that could be extrapolated to other GP systems, for effective and simple bloat control.作者: 搖晃 時(shí)間: 2025-3-22 17:10 作者: 舞蹈編排 時(shí)間: 2025-3-22 23:35
Conference proceedings 20142014 co-located with the Evo*2014 events, Evo BIO, Evo COP, Evo MUSART and Evo Applications..The 15 revised full papers presented together with 5 poster papers were carefully reviewed and selected form 40 submissions. The wide range of topics in this volume reflects the current state of research in 作者: RODE 時(shí)間: 2025-3-23 03:50 作者: ADORN 時(shí)間: 2025-3-23 07:37 作者: 考古學(xué) 時(shí)間: 2025-3-23 11:31
Semantic Crossover Based on the Partial Derivative Errorhrough the tree. This process decides the crossing point of the second parent. The results show that our procedure improves the performance of genetic programming on rational symbolic regression problems.作者: 啪心兒跳動(dòng) 時(shí)間: 2025-3-23 15:42
The Best Things Don’t Always Come in Small Packages: Constant Creation in Grammatical Evolutioner..The results are surprising. The GE methods all perform significantly better than GP on unseen test data, and we demonstrate that the standard GE approach of . does not produce individuals that are any larger than those from methods which are designed to use less genetic material.作者: Pde5-Inhibitors 時(shí)間: 2025-3-23 21:14 作者: 褪色 時(shí)間: 2025-3-24 01:23 作者: Indurate 時(shí)間: 2025-3-24 04:25 作者: 季雨 時(shí)間: 2025-3-24 09:10
Informationen und Informationsmodelle,istance measure. This paper investigates the use of Genetic Programming for the evolution of task-specific distance measures as an alternative to Euclidean distance. Results on seven real-world datasets show that the generalisation performance of the proposed system is superior to that of Euclidean-based kernel regression and standard GP.作者: 發(fā)酵 時(shí)間: 2025-3-24 10:41 作者: Regurgitation 時(shí)間: 2025-3-24 18:24 作者: Sciatica 時(shí)間: 2025-3-24 23:04 作者: 太空 時(shí)間: 2025-3-25 01:14
CGP does not suffer from bloat. It has also been shown for CGP that using a very large number of nodes considerably increases the effectiveness of the search. This paper also proposes a new explanation as to why this may be the case.作者: cancer 時(shí)間: 2025-3-25 06:19
UI Design Considerations for Data Entry, not only makes GP more robust, but it also provides an informed online means of halting the learning process. Flash enables GP to learn from a dataset composed of 370K exemplars and 90 features, evolving a population of 1000 individuals over 100 generations in as few as 50 seconds.作者: 后退 時(shí)間: 2025-3-25 07:46 作者: Parley 時(shí)間: 2025-3-25 13:23 作者: bile648 時(shí)間: 2025-3-25 16:33 作者: 妨礙 時(shí)間: 2025-3-25 22:18
A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problemsion problems using GP, which may lead to further research in this direction. We test the new approach on a large set of benchmark problems from several different sources, and observe its competitiveness against the most successful state-of-the-art classifiers.作者: GROVE 時(shí)間: 2025-3-26 00:43 作者: 歹徒 時(shí)間: 2025-3-26 06:24 作者: 首創(chuàng)精神 時(shí)間: 2025-3-26 09:01
UI Design Considerations for Data Entry,st of the extensive model predictions required by symbolic regression, its fitness evaluations are tasked to the desktop’s GPU. Successive GP “instances” are run on different data subsets and randomly chosen objective functions. Best models are collected after a fixed number of generations and then 作者: 提名 時(shí)間: 2025-3-26 13:05 作者: 忘川河 時(shí)間: 2025-3-26 19:23 作者: 確定無疑 時(shí)間: 2025-3-26 23:38 作者: 不可接觸 時(shí)間: 2025-3-27 02:55
https://doi.org/10.1007/978-94-011-0637-5. The input space . is composed of observational data of the form (.., .(..)), .?=?1... . where each .. denotes a k-dimensional input vector of design variables and . is the response. Genetic Programming (GP) is used to transform the original input space . into a new input space .?=?(.., .(..)) that作者: Debark 時(shí)間: 2025-3-27 07:17
https://doi.org/10.1007/978-1-4842-9253-2stems, and evolutionary robotics. To date most research on this task has been described in terms of developments to reinforcement learning with function approximation or frameworks for neuro-evolution. This work performs an initial study using a recently proposed algorithm for evolving teams of prog作者: frivolous 時(shí)間: 2025-3-27 10:31 作者: 蟄伏 時(shí)間: 2025-3-27 16:09 作者: LAPSE 時(shí)間: 2025-3-27 19:51
https://doi.org/10.1007/978-3-662-67556-4icroprogram. Linear genetic programming is extended to evolve a program for the controller together with suitable hardware architecture. Experimental results show that the platform can automatically design general solutions as well as highly optimized specialized solutions to benchmark problems such作者: 跳動(dòng) 時(shí)間: 2025-3-27 22:35
Dominik Lis,Joshua Gelhaar,Boris Ottodecisions allows the systems to utilise their manufacturing resources better and achieve higher total profit. Therefore, finding optimal solutions for OAS is desirable. Unfortunately, the exact optimisation approaches previously proposed for OAS are still very time consuming and usually fail to solv作者: GLIDE 時(shí)間: 2025-3-28 03:03 作者: acrimony 時(shí)間: 2025-3-28 07:08 作者: Geyser 時(shí)間: 2025-3-28 11:29 作者: obsolete 時(shí)間: 2025-3-28 15:38 作者: mydriatic 時(shí)間: 2025-3-28 19:44 作者: 藥物 時(shí)間: 2025-3-29 02:37
Implementation Tools of IoT Systems,als at the same time. ARE is designed for an asynchronous evolution by tertiary parent selection and its archive. In particular, ARE asynchronously evolves individuals through a comparison with only three of individuals (i.e., two parents and one . individual as the tertiary parent). In addition, AR作者: 過去分詞 時(shí)間: 2025-3-29 06:26 作者: Jacket 時(shí)間: 2025-3-29 08:32 作者: 侵略 時(shí)間: 2025-3-29 13:43
978-3-662-44302-6Springer-Verlag Berlin Heidelberg 2014作者: periodontitis 時(shí)間: 2025-3-29 18:23
Genetic Programming978-3-662-44303-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Palpate 時(shí)間: 2025-3-29 21:32
Gerhard Grunwald,Gregory D. Hagerefactor legacy GPGPU C?code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards are reported compared to the original kernel on the same hardware.作者: Vertebra 時(shí)間: 2025-3-30 00:04 作者: 裙帶關(guān)系 時(shí)間: 2025-3-30 07:00
Genetically Improved CUDA C++ Softwareefactor legacy GPGPU C?code for modern parallel graphics hardware and software. Speed ups of more than six times on recent nVidia GPU cards are reported compared to the original kernel on the same hardware.作者: 極小 時(shí)間: 2025-3-30 09:16
Exploring the Search Space of Hardware / Software Embedded Systems by Means of GPicroprogram. Linear genetic programming is extended to evolve a program for the controller together with suitable hardware architecture. Experimental results show that the platform can automatically design general solutions as well as highly optimized specialized solutions to benchmark problems such as maximum, parity or iterative division.作者: 使入迷 時(shí)間: 2025-3-30 15:51 作者: ALIAS 時(shí)間: 2025-3-30 17:21 作者: investigate 時(shí)間: 2025-3-30 21:52 作者: Intercept 時(shí)間: 2025-3-31 04:19 作者: 記憶法 時(shí)間: 2025-3-31 07:01
Semantic Crossover Based on the Partial Derivative Errors have traditionally been developed employing experimentally or theoretically-based approaches. Our current work proposes a novel semantic crossover developed amid the two traditional approaches. Our proposed semantic crossover operator is based on the use of the derivative of the error propagated t作者: trigger 時(shí)間: 2025-3-31 12:05
A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems classification problems can be very complex, in particular when the number of classes is high. Although very successful in so many applications, GP was never regarded as a good method to perform multi-class classification. In this work, we present a novel algorithm for tree based GP, that incorpora作者: 拋棄的貨物 時(shí)間: 2025-3-31 16:30
