Natural Computing最新文献_第8页

Deep learning networks with rough-refinement optimization for food quality assessment 用于食品质量评估的粗糙-精细优化深度学习网络

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-16 DOI: 10.1007/s11047-022-09890-6

Jingrun Zhou, Kang Zhou, G. Zhang, Qiyu Liu, Wangyang Shen, Weiping Jin

引用次数: 2

MOEA/D with gradient-enhanced kriging for expensive multiobjective optimization 基于梯度增强kriging的MOEA/D多目标优化

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-13 DOI: 10.1007/s11047-022-09907-0

Fei Liu, Qingfu Zhang, Zhonghua Han

引用次数: 3

GPU simulations of spiking neural P systems on modern web browsers 现代网络浏览器上的神经脉冲系统的GPU模拟

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-12 DOI: 10.1007/s11047-022-09914-1

Arian Allenson M. Valdez, Filbert Wee, A. N. L. Odasco, Matthew Lemuel M. Rey, F. Cabarle

引用次数: 3

Large population sizes and crossover help in dynamic environments 庞大的人口规模和跨界有助于在动态环境中

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-11 DOI: 10.1007/s11047-022-09915-0

Johannes Lengler, Jonas Meier

{"title":"Large population sizes and crossover help in dynamic environments","authors":"Johannes Lengler, Jonas Meier","doi":"10.1007/s11047-022-09915-0","DOIUrl":"https://doi.org/10.1007/s11047-022-09915-0","url":null,"abstract":"Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the ((1+1))-Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for dynamic binval, the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for ((mu +1))-EA is not close the optimum, but far away from it. In contrast, for the ((mu +1))-GA, the region around the optimum is the hardest region in all studied cases.Kindly check and confirm the inserted city name is correctly identified.Correct.","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":"6 3","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problem 基于蒙特卡罗片段插入和构象聚类的蛋白质结构预测自适应进化算法

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-11 DOI: 10.1007/s11047-022-09916-z

R. S. Parpinelli, Nilcimar Neitzel Will, Renan Samuel da Silva

引用次数: 0

Spiking neural P systems and their semantics in Haskell Haskell中的脉冲神经P系统及其语义

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-08-10 DOI: 10.1007/s11047-022-09897-z

Gabriel Ciobanu, E. Todoran

引用次数: 2

A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization 进化多目标优化中增强松弛Pareto优势方法的通用框架

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-07-27 DOI: 10.1007/s11047-022-09889-z

Shuwei Zhu, Lihong Xu, E. Goodman, K. Deb, Zhichao Lu

引用次数: 1

Preface 前言

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-07-26 DOI: 10.1007/s11047-022-09898-y

T. Gwizdalla, L. Manzoni, G. Mauri

引用次数: 0

Exponential separation between quantum and classical ordered binary decision diagrams, reordering method and hierarchies 量子和经典有序二元决策图的指数分离，重排序方法和层次

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-07-26 DOI: 10.1007/s11047-022-09904-3

Kamil Khadiev, Aliya Khadieva, Alexander Knop

{"title":"Exponential separation between quantum and classical ordered binary decision diagrams, reordering method and hierarchies","authors":"Kamil Khadiev, Aliya Khadieva, Alexander Knop","doi":"10.1007/s11047-022-09904-3","DOIUrl":"https://doi.org/10.1007/s11047-022-09904-3","url":null,"abstract":"In this paper, we study quantum Ordered Binary Decision Diagrams((mathrm {OBDD})) model; it is a restricted version of read-once quantum branching programs, with respect to “width” complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique (“reordering”) for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function (mathrm {REQ}) such that the deterministic (mathrm {OBDD}) complexity of it is at least (2^{varOmega (n / log n)}), and the quantum (mathrm {OBDD}) complexity of it is at most (O(n^2/log n)). It is the biggest known gap for explicit functions not representable by (mathrm {OBDD})s of a linear width. Another function(shifted equality function) allows us to obtain a gap (2^{varOmega (n)}) vs (O(n^2)). Moreover, we prove the bounded error quantum and probabilistic (mathrm {OBDD}) width hierarchies for complexity classes of Boolean functions. Additionally, using “reordering” method we extend a hierarchy for read-k-times Ordered Binary Decision Diagrams (({textit{k}}text {-}mathrm {OBDD})) of polynomial width, for (k = o(n / log ^3 n)). We prove a similar hierarchy for bounded error probabilistic ({textit{k}}text {-}mathrm {OBDD})s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017)","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":"11 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Face illumination normalization based on generative adversarial network 基于生成对抗网络的人脸照明归一化

IF 2.1 4区计算机科学

Natural Computing Pub Date : 2022-07-20 DOI: 10.1007/s11047-022-09892-4

Dequan Guo, Lingrui Zhu, Shenggui Ling, Tianxiang Li, G. Zhang, Qiang Yang, Ping Wang, Shiqi Jiang, Sidong Wu, Junbao Liu

引用次数: 2