作者: 來(lái)就得意 時(shí)間: 2025-3-21 20:49
https://doi.org/10.1007/978-3-662-26496-6his study, we present the application of GP to predict the distribution of ., a mosquito species vector of West Nile virus (WNV), in Piedmont, Italy. Our modelling approach took into consideration the ecological factors which affect mosquitoes abundance. Our results showed that GP was able to outper作者: Rustproof 時(shí)間: 2025-3-22 04:00 作者: 毗鄰 時(shí)間: 2025-3-22 06:05 作者: menopause 時(shí)間: 2025-3-22 11:41
https://doi.org/10.1007/978-3-662-39789-3agents are spawned from multiple different locations. A unique approach is adopted to defining external memory for genetic programming agents in which: (1) the state of memory is shared across all programs. (2) Writing is formulated as a probabilistic process, resulting in different regions of memor作者: 易受騙 時(shí)間: 2025-3-22 14:36
https://doi.org/10.1007/978-3-662-26494-2mbedded feature selection and multi-label classification, has not been explored to solve this problem. In this paper, we propose a linear GP (LGP) algorithm to search predictive models for motor fault detection and classification. Our method is able to evolve multi-label classifiers with high accura作者: 易受騙 時(shí)間: 2025-3-22 19:38
Falk Würfele,Alexander Muchowski, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrat作者: 巨頭 時(shí)間: 2025-3-23 01:10 作者: 剝削 時(shí)間: 2025-3-23 04:02
,Perspektive Hoffnung für das Dorf,rogramming, which has not been used for design of some of these functions before, is the best at dealing with increasing number of inputs, and creates desired functions with better reliability than the commonly used methods.作者: Obloquy 時(shí)間: 2025-3-23 07:32
https://doi.org/10.1007/978-3-531-90581-5tions itself, but rather in evolving some of their components, i.e. bent functions. Finally, we present an additional parameter to evaluate the performance of evolutionary algorithms when evolving Boolean functions: the diversity of the obtained solutions.作者: NOVA 時(shí)間: 2025-3-23 12:27 作者: covert 時(shí)間: 2025-3-23 17:15 作者: DUST 時(shí)間: 2025-3-23 21:55
Towards a Scalable EA-Based Optimization of Digital Circuitsation. Our evaluation on a set of nontrivial real-world benchmark problems shows that the proposed method provides better results compared to global evolutionary optimization. In more than 60% cases, substantially higher number of redundant gates was removed while keeping the computational effort at作者: Accede 時(shí)間: 2025-3-23 23:32
Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Prt the user can define conditions when a particular CGP individual is acceptable. We evaluated SCGP on four common symbolic regression benchmark problems and the obtained node reduction is from 92.4% to 99.9%.作者: Asymptomatic 時(shí)間: 2025-3-24 06:08
A Model of External Memory for Navigation in Partially Observable Visual Reinforcement Learning Taskagents are spawned from multiple different locations. A unique approach is adopted to defining external memory for genetic programming agents in which: (1) the state of memory is shared across all programs. (2) Writing is formulated as a probabilistic process, resulting in different regions of memor作者: Adenoma 時(shí)間: 2025-3-24 09:17
Fault Detection and Classification for Induction Motors Using Genetic Programmingmbedded feature selection and multi-label classification, has not been explored to solve this problem. In this paper, we propose a linear GP (LGP) algorithm to search predictive models for motor fault detection and classification. Our method is able to evolve multi-label classifiers with high accura作者: 充足 時(shí)間: 2025-3-24 12:02
Fast DENSER: Efficient Deep NeuroEvolution, or to solve various problems the user only needs to change the grammar that is specified in a text human-readable format. The new method, Fast DENSER (F-DENSER), speeds up DENSER, and adds another representation-level that allows the connectivity of the layers to be evolved. The results demonstrat作者: Lumbar-Spine 時(shí)間: 2025-3-24 16:52 作者: 低能兒 時(shí)間: 2025-3-24 20:20
Comparison of Genetic Programming Methods on Design of Cryptographic Boolean Functionsrogramming, which has not been used for design of some of these functions before, is the best at dealing with increasing number of inputs, and creates desired functions with better reliability than the commonly used methods.作者: 背帶 時(shí)間: 2025-3-25 00:00 作者: 不易燃 時(shí)間: 2025-3-25 04:10 作者: OGLE 時(shí)間: 2025-3-25 08:31 作者: 闡釋 時(shí)間: 2025-3-25 13:34 作者: 冷淡周邊 時(shí)間: 2025-3-25 17:06 作者: 模仿 時(shí)間: 2025-3-25 20:07 作者: chalice 時(shí)間: 2025-3-26 01:53 作者: FADE 時(shí)間: 2025-3-26 04:21
https://doi.org/10.1007/978-3-322-98823-2ia grammatical evolution. We focus on novelty search – substituting the conventional search objective – based on synthesis quality, with a novelty objective. This prompts us to introduce a new selection method named .. It parametrically balances exploration and exploitation by creating a mixed popul作者: 認(rèn)識(shí) 時(shí)間: 2025-3-26 11:09
https://doi.org/10.1007/978-3-531-90243-2es. Recently, various formal approaches have been introduced to this field to overcome this issue. This made it possible to optimise complex circuits consisting of hundreds of inputs and thousands of gates. Unfortunately, we are facing to the another problem – scalability of representation. The effi作者: 責(zé)難 時(shí)間: 2025-3-26 14:50
,Wege zur Ruhe und Kreativit?t,tages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian作者: 反話 時(shí)間: 2025-3-26 20:31
https://doi.org/10.1007/978-3-663-06927-0tion, such as manifold learning, is often used to reduce the number of features in a dataset to a manageable level for human interpretation. Despite this, most manifold learning techniques do not explain anything about the original features nor the true characteristics of a dataset. In this paper, w作者: 使習(xí)慣于 時(shí)間: 2025-3-27 00:37 作者: 全能 時(shí)間: 2025-3-27 05:10 作者: Expiration 時(shí)間: 2025-3-27 06:52 作者: 善于 時(shí)間: 2025-3-27 11:02
https://doi.org/10.1007/978-3-662-26494-2al industrial processes. Since various types of mechanical and electrical faults could occur, induction motor fault diagnosis can be interpreted as a multi-label classification problem. The current and vibration input data collected by monitoring a motor often require signal processing to extract fe作者: 千篇一律 時(shí)間: 2025-3-27 13:57
Falk Würfele,Alexander Muchowskiwhere we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of su作者: BET 時(shí)間: 2025-3-27 17:55
https://doi.org/10.1007/978-3-663-14479-3popular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a . representation where each observation corresponds to a collection on a subject at a 作者: coagulate 時(shí)間: 2025-3-27 23:15
,Perspektive Hoffnung für das Dorf, data by utilizing a pseudo-randomly generated binary sequence. Generating a cryptographically secure sequence is not an easy task and requires a Boolean function possessing multiple cryptographic properties. One of the most successful ways of designing these functions is genetic programming. In thi作者: 語(yǔ)言學(xué) 時(shí)間: 2025-3-28 04:43
https://doi.org/10.1007/978-3-531-90581-5 allowed by Parseval’s relation. Hyper-bent functions, in turn, are those bent functions which additionally reach maximum distance from all bijective monomial functions, and provide further security towards approximation attacks. Being characterized by a stricter definition, hyper-bent functions are作者: 蒼白 時(shí)間: 2025-3-28 10:19
https://doi.org/10.1007/978-3-8349-9508-7Using 512?bit Advanced Vector Extensions, previous development history and Intel documentation, BNF grammar based genetic improvement automatically ports RNAfold to AVX, giving up?to a 1.77 fold speed up. The evolved code pull request is an accepted GI software maintenance update to bioinformatics package ..作者: Horizon 時(shí)間: 2025-3-28 12:28 作者: 捏造 時(shí)間: 2025-3-28 18:34
https://doi.org/10.1007/978-3-030-16670-0artificial intelligence; Boolean function; Cartesian genetic programming; cryptography; data mining; evol作者: 黃油沒(méi)有 時(shí)間: 2025-3-28 22:13
Machteroberung und Machtsicherung,ent approaches to unsupervised learning. Here, we use the genetic programming paradigm to create autoencoders and find that the task is difficult for genetic programming, even on small datasets which are easy for neural networks. We investigate which aspects of the autoencoding task are difficult for genetic programming.作者: Bone-Scan 時(shí)間: 2025-3-29 02:10 作者: 尾隨 時(shí)間: 2025-3-29 04:46 作者: 陶器 時(shí)間: 2025-3-29 08:43
Quantum Program Synthesis: Swarm Algorithms and Benchmarksthe number of quantum algorithms. Hence, there is a great deal of interest in the automatic synthesis of quantum circuits and algorithms. Here we present a set of experiments which use Ant Programming to automatically synthesise quantum circuits. In the proposed approach, ants choosing paths in high作者: ANTH 時(shí)間: 2025-3-29 15:18 作者: 詞根詞綴法 時(shí)間: 2025-3-29 15:59 作者: 使激動(dòng) 時(shí)間: 2025-3-29 20:40 作者: motivate 時(shí)間: 2025-3-30 01:16 作者: 帳單 時(shí)間: 2025-3-30 08:05
Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Prtages, including much higher quality of resulting individuals (in terms of error) in comparison with a common genetic programming. However, GSGP produces extremely huge solutions that could be difficult to apply in systems with limited resources such as embedded systems. We propose Subtree Cartesian作者: 使迷醉 時(shí)間: 2025-3-30 09:25 作者: 倫理學(xué) 時(shí)間: 2025-3-30 14:56 作者: 值得尊敬 時(shí)間: 2025-3-30 19:00
Solution and Fitness Evolution (SAFE): Coevolving Solutions and Their Objective Functionsen when the former is well defined, the latter may not be obvious, e.g., in learning a strategy to navigate a maze to find a goal (objective), an effective objective function to . strategies may not be a simple function of the distance to the objective. We proposed to automate the means by which a g作者: AER 時(shí)間: 2025-3-30 23:18
A Model of External Memory for Navigation in Partially Observable Visual Reinforcement Learning Tasktion takes the form of high-dimensional data, such as video. In addition, although the video might characterize a 3D world in high resolution, partial observability will place significant limits on what the agent can actually perceive of the world. This means that the agent also has to: (1) provide 作者: 下邊深陷 時(shí)間: 2025-3-31 02:28 作者: Recess 時(shí)間: 2025-3-31 08:28
Fast DENSER: Efficient Deep NeuroEvolutionwhere we have to make decisions about the topology of the network, learning algorithm, and numerical parameters. To ease this process, we can resort to methods that seek to automatically optimise either the topology or simultaneously the topology and learning parameters of ANNs. The main issue of su作者: neurologist 時(shí)間: 2025-3-31 10:40
A Vectorial Approach to Genetic Programmingpopular. A common situation consists in the prediction of a target time series based on scalar features and other time series variables collected from multiple subjects. To manage this problem with GP data needs a . representation where each observation corresponds to a collection on a subject at a 作者: 改變 時(shí)間: 2025-3-31 15:17 作者: Ossification 時(shí)間: 2025-3-31 20:55 作者: Heart-Rate 時(shí)間: 2025-4-1 01:11
0302-9743 19, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications...The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and?reflect the current 作者: conjunctiva 時(shí)間: 2025-4-1 05:44 作者: 窩轉(zhuǎn)脊椎動(dòng)物 時(shí)間: 2025-4-1 09:40
Improving Genetic Programming with Novel Exploration - Exploitation Controlation of parents. One subset is chosen based on performance quality and the other subset is chosen based on diversity. Three versions of this method, two that adaptively tune balance during evolution solve program synthesis problems more accurately, faster and with less duplication than grammatical evolution with lexicase selection.
