標(biāo)題: Titlebook: Estimation of Distribution Algorithms; A New Tool for Evolu Pedro Larra?aga,Jose A. Lozano Book 2002 Springer Science+Business Media New Yo [打印本頁] 作者: Stubborn 時(shí)間: 2025-3-21 18:32
書目名稱Estimation of Distribution Algorithms影響因子(影響力)
書目名稱Estimation of Distribution Algorithms影響因子(影響力)學(xué)科排名
書目名稱Estimation of Distribution Algorithms網(wǎng)絡(luò)公開度
書目名稱Estimation of Distribution Algorithms網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Estimation of Distribution Algorithms被引頻次
書目名稱Estimation of Distribution Algorithms被引頻次學(xué)科排名
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書目名稱Estimation of Distribution Algorithms年度引用學(xué)科排名
書目名稱Estimation of Distribution Algorithms讀者反饋
書目名稱Estimation of Distribution Algorithms讀者反饋學(xué)科排名
作者: separate 時(shí)間: 2025-3-21 22:29 作者: Retrieval 時(shí)間: 2025-3-22 04:19 作者: 符合規(guī)定 時(shí)間: 2025-3-22 04:51 作者: Little 時(shí)間: 2025-3-22 10:41
Dario Braga,Fabrizia Grepioni,A. Guy Orpenhe most used Evolutionary Algorithms —Genetic Algorithms, Evolution Strategies and Evolutionary Programming— are explained in detail. We give pointers to the literature on their theoretical foundations.作者: 光亮 時(shí)間: 2025-3-22 14:53 作者: 光亮 時(shí)間: 2025-3-22 17:08
https://doi.org/10.1007/978-3-642-95686-7 in continuous domains. Different approaches for Estimation of Distribution Algorithms have been ordered by the complexity of the interrelations that they are able to express. These will be introduced using one unified notation.作者: 四海為家的人 時(shí)間: 2025-3-23 00:52 作者: Engaging 時(shí)間: 2025-3-23 03:15
https://doi.org/10.1007/978-1-4757-4896-3etworks to model the probability distribution of the selected individuals, and particularly on those that use a score+search learning strategy. Apart from the evaluation of the fitness function, the biggest computational cost in these EDAs is due to the structure learning step. We aim to speed up th作者: 項(xiàng)目 時(shí)間: 2025-3-23 06:17 作者: Benzodiazepines 時(shí)間: 2025-3-23 10:59 作者: Gyrate 時(shí)間: 2025-3-23 14:50 作者: 充滿人 時(shí)間: 2025-3-23 18:10
https://doi.org/10.1007/978-94-011-6056-8ferent types of representation, three methods for obtaining the initial population and two different methods for handling the problem’s constraints. Experimental results for problems of different sizes are given.作者: institute 時(shí)間: 2025-3-23 22:20 作者: Nefarious 時(shí)間: 2025-3-24 04:00 作者: 虛弱的神經(jīng) 時(shí)間: 2025-3-24 09:47
https://doi.org/10.1007/978-3-319-65912-1le of its use is in image recognition problems, where structures to be recognized are represented by nodes in a graph that are matched against a model, which is also represented as a graph..As the number of image recognition areas that make use of graphs is increasing, new techniques are being intro作者: Guaff豪情痛飲 時(shí)間: 2025-3-24 10:51 作者: 善變 時(shí)間: 2025-3-24 18:17 作者: CBC471 時(shí)間: 2025-3-24 20:43
James R. Cullen,M. Melamud,K. HathawayEach individual obtained by simulation of the probability distribution learnt in each EDA generation represents a disjunction of a finite number of simple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UM作者: 四牛在彎曲 時(shí)間: 2025-3-25 03:10
Crystalline Metal Oxide Catalystset of the network variables ., given some observations . This problem, also known as the ., is known to be NP-hard, so exact computation is not always possible. As partial abductive inference in Bayesian networks can be viewed as a combinatorial optimization problem, Genetic Algorithms have been suc作者: 絕種 時(shí)間: 2025-3-25 05:16 作者: 反復(fù)無常 時(shí)間: 2025-3-25 09:48 作者: 樹上結(jié)蜜糖 時(shí)間: 2025-3-25 15:02
https://doi.org/10.1007/978-3-642-95686-7 in continuous domains. Different approaches for Estimation of Distribution Algorithms have been ordered by the complexity of the interrelations that they are able to express. These will be introduced using one unified notation.作者: 受辱 時(shí)間: 2025-3-25 19:00
https://doi.org/10.1007/978-1-4757-1272-8rature by introducing them into two general frameworks: Markov chains and dynamical systems. In addition, we use Markov chains to give a general convergence result for discrete EDAs. Some discrete EDAs are analyzed using this result, to obtain sufficient conditions for convergence.作者: 盲信者 時(shí)間: 2025-3-25 20:19 作者: 使激動(dòng) 時(shí)間: 2025-3-26 02:00
Richard E. Stoiber,Stearns A. Morses UMDA., MIMIC., EGNABIc, EGNABG., EGNAee, EMNA.lob, 1, and EMNA. algorithms were implemented. Their performance was compared to such of Evolution Strategies (Schwefel, 1995). The optimization problems of choice were Summation cancellation, Griewangk, Sphere model, Rosenbrock generalized, and Ackley.作者: Perigee 時(shí)間: 2025-3-26 06:47
https://doi.org/10.1007/978-94-011-6056-8ferent types of representation, three methods for obtaining the initial population and two different methods for handling the problem’s constraints. Experimental results for problems of different sizes are given.作者: iodides 時(shí)間: 2025-3-26 10:41
The Calculation of the Cosine Seminvariantsgorithms literature the most successful codifications and hybridizations. Estimation of Distribution Algorithms are plainly applied with these elements in the Fisher and Thompson (1963) datasets. The results are comparable with those obtained with Genetic Algorithms.作者: CREST 時(shí)間: 2025-3-26 15:44
An Introduction to Evolutionary Algorithmshe most used Evolutionary Algorithms —Genetic Algorithms, Evolution Strategies and Evolutionary Programming— are explained in detail. We give pointers to the literature on their theoretical foundations.作者: 保全 時(shí)間: 2025-3-26 18:43 作者: MIR 時(shí)間: 2025-3-27 00:43
A Review on Estimation of Distribution Algorithms in continuous domains. Different approaches for Estimation of Distribution Algorithms have been ordered by the complexity of the interrelations that they are able to express. These will be introduced using one unified notation.作者: 凝視 時(shí)間: 2025-3-27 02:15
Mathematical Modeling of Discrete Estimation of Distribution Algorithmsrature by introducing them into two general frameworks: Markov chains and dynamical systems. In addition, we use Markov chains to give a general convergence result for discrete EDAs. Some discrete EDAs are analyzed using this result, to obtain sufficient conditions for convergence.作者: 衰弱的心 時(shí)間: 2025-3-27 06:31
An Empirical Comparison of Discrete Estimation of Distribution Algorithmsempirical comparison is carried out in relation with three different criteria: the convergence velocity, the convergence reliability and the scalability. Different function sets are optimized depending on the aspect to evaluate.作者: Osteoporosis 時(shí)間: 2025-3-27 10:13 作者: 填滿 時(shí)間: 2025-3-27 15:56 作者: Jubilation 時(shí)間: 2025-3-27 21:27 作者: gratify 時(shí)間: 2025-3-28 01:26
Book 2002thispart, after introducing some probabilistic graphical models -Bayesian and Gaussian networks - a review of existing EDAapproaches is presented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II cove作者: tenosynovitis 時(shí)間: 2025-3-28 03:43
1568-2587 ented, as well as some new methods based on moreflexible probabilistic graphical models. A mathematical modeling ofdiscrete EDAs is also presented. Part II cove978-1-4613-5604-2978-1-4615-1539-5Series ISSN 1568-2587 作者: 提煉 時(shí)間: 2025-3-28 07:42 作者: 生存環(huán)境 時(shí)間: 2025-3-28 12:45 作者: Thyroid-Gland 時(shí)間: 2025-3-28 14:54
Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms for the Nearest Neighbor algorithm. While the FW-EBNA has a set of three possible discrete weights, the FW-EGNA works in a continuous range of weights. Both methods are compared in a set of natural and artificial domains with two sequential and one Genetic Algorithm.作者: Vital-Signs 時(shí)間: 2025-3-28 19:30
Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAscessfully applied to give an approximate algorithm for it (de Campos et al., 1999). In this work we approach the problem by means of Estimation of Distribution Algorithms, and an empirical comparison between the results obtained by Genetic Algorithms and Estimation of Distribution Algorithms is carried out.作者: 災(zāi)難 時(shí)間: 2025-3-28 23:43 作者: Matrimony 時(shí)間: 2025-3-29 06:57
Solving the Traveling Salesman Problem with EDAsarch) is combined with EDAs to find better solutions. We show experimental results obtained on several standard examples for discrete and continuous EDAs both alone and combined with a heuristic local search.作者: Adenoma 時(shí)間: 2025-3-29 08:47
Rule Induction by Estimation of Distribution Algorithmsmple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UMDA, a dependency tree and EBNAwith two classical algorithms of rule induction —RIPPER and CN2— are shown.作者: Brain-Imaging 時(shí)間: 2025-3-29 12:25 作者: DNR215 時(shí)間: 2025-3-29 17:29 作者: Promotion 時(shí)間: 2025-3-29 21:45
M. Kasaya,K. Takegahara,A. Yanase,T. Kasuya for the Nearest Neighbor algorithm. While the FW-EBNA has a set of three possible discrete weights, the FW-EGNA works in a continuous range of weights. Both methods are compared in a set of natural and artificial domains with two sequential and one Genetic Algorithm.作者: 誘惑 時(shí)間: 2025-3-30 03:41 作者: 釋放 時(shí)間: 2025-3-30 07:50 作者: abolish 時(shí)間: 2025-3-30 09:50
https://doi.org/10.1007/978-1-4757-4896-3from the evaluation of the fitness function, the biggest computational cost in these EDAs is due to the structure learning step. We aim to speed up the structure learning step by the use of parallelism. Two different approaches will be given and evaluated experimentally in a shared memory MIMD computer.作者: 先驅(qū) 時(shí)間: 2025-3-30 14:17
https://doi.org/10.1007/978-3-642-78208-4arch) is combined with EDAs to find better solutions. We show experimental results obtained on several standard examples for discrete and continuous EDAs both alone and combined with a heuristic local search.作者: 令人心醉 時(shí)間: 2025-3-30 19:09
James R. Cullen,M. Melamud,K. Hathawaymple rules. This problem has been modeled to allow representations with different complexities. Experimental results comparing three types of EDAs —UMDA, a dependency tree and EBNAwith two classical algorithms of rule induction —RIPPER and CN2— are shown.作者: 特別容易碎 時(shí)間: 2025-3-30 21:22
Book 2002mation of distribution algorithms(EDAs). This new class of algorithms generalizes genetic algorithms byreplacing the crossover and mutation operators with learning andsampling from the probability distribution of the best individuals ofthe population at each iteration of the algorithm. Working in su作者: legislate 時(shí)間: 2025-3-31 04:03
