检索结果-内蒙古大学图书馆

19th European Conference on the Applications of Evolutionary Computation (EvoApplications)

作者： Garcia-Sanchez, Pablo Ortega, Julio Gonzalez, Jesus Castillo, Pedro A. Merelo, Juan J. Univ Granada Dept Comp Architecture & Technol Granada Spain

ISBN: (纸本)9783319311531

Large-scale multi-objective optimization problems with many decision variables have recently attracted the attention of researchers as many data mining applications involving high dimensional patterns can be leveraged using them. Current parallel and distributed computer architectures can provide the required computing capabilities to cope with these problems once efficient procedures are available. In this paper we propose a cooperative coevolutionary island-model procedure based on the parallel execution of sub-populations, whose individuals explore different domains of the decision variables space. More specifically, the individuals belonging to the same sub-population (island) explore the same subset of decision variables. Two alternatives to distribute the decision variables among the different sub-populations have been considered and compared here. In the first approach, individuals in different sub-population explore disjoint subsets of decision variables (i.e. the chromosomes are divided into disjoints subsets). Otherwise, in the second alternative there are some overlapping among the variables explored by individuals in different sub-populations. The analysis of the obtained experimental results, by using different metrics, shows that coevolutionary approaches provide statistically significant improvements with respect to the base algorithm, being the relation of the number of islands (subpopulations) to the length of the chromosome (number of decision variables) a relevant factor to determine the most efficient alternative to distribute the decision variables.

关键词： multi-objective algorithms NSGA-II Island model Distributed evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Energy Efficient Routing Based on NSGA-II for Context Parameters Aware Sensor Networks 14th

Energy Efficient Routing Based on NSGA-II for Context Parame...

引用

14th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Rodriguez, Angela Ordonez, Armando Ordonez, Hugo Fdn Univ Popayan Popayan Colombia Univ San Buenaventura Cali Colombia

ISBN: (纸本)9783319270609;9783319270593

A wireless sensor network collects crucial data for decision making in several domains even under extreme deployment conditions. In this scenario, network availability is usually affected by diverse environment variables. The present approach adapts an evolutionary multi-objective technique in order to get network structures that let to perform data routing efficient in energy consumption. The resulting algorithm, MOR4WSN, comes up from a new solution encoding done to the NSGA-II as well as adapting user-preferences handling even if preference context parameters to optimize are contradictory. MOR4WSN allows optimizing data gathering paths, which contributes to increase network longevity. Experimental evaluation shows that network lifecycle is increased when MOR4WSN is used, compared to other routing mechanisms.

关键词： Evolutionary optimization techniques Sensor networks multi-objective algorithms

来源：评论

学校读者我要写书评

暂无评论

Search Based Design of Software Product Lines Architectures

Search Based Design of Software Product Lines Architectures

引用

34th International Conference on Software Engineering (ICSE)

作者： Colanzi, Thelma Elita Univ Fed Parana UFPR Dept Comp Sci BR-80060000 Curitiba Parana Brazil

ISBN: (纸本)9781467310673

The Product-Line Architecture (PLA) is the main artifact of a Software Product Line (SPL). However, obtaining a modular, extensible and reusable PLA is a people-intensive and non-trivial task, related to different and possible conflicting factors. Hence, the PLA design is a hard problem and to find the best architecture can be formulated as an optimization problem with many factors. Similar Software Engineering problems have been efficiently solved by search-based algorithms in the field known as Search-based Software Engineering. The existing approaches used to optimize software architecture are not suitable since they do not encompass specific characteristics of SPL. To easy the SPL development and to automate the PLA design this work introduces a multi-objective optimization approach to the PLA design. The approach is now being implemented by using evolutionary algorithms. Empirical studies will be performed to validate the neighborhood operators, SPL measures and search algorithms chosen. Finally, we intend to compare the results of the proposed approach with PLAs designed by human architects.

关键词： software product lines multi-objective algorithms software architecture optimization

来源：评论

学校读者我要写书评

暂无评论

Implementace evolučního expertního systému

Implementace evolučního expertního systému

引用

作者： Bukáček, Jan Brno University of Technology

Tato diplomová práce je zaměřena na nastudování problematiky evolučních a genetických algoritmů. A to především na multikriteriální algoritmy VEGA, SPEA a NSGA-II. Dá... 详细信息

Tato diplomová práce je zaměřena na nastudování problematiky evolučních a genetických algoritmů. A to především na multikriteriální algoritmy VEGA, SPEA a NSGA-II. Dále jednoho FrameWorku pracujícího s genetickými algoritmy a to jmenovitě WWW NIMBUS. Z uvedených algoritmů je vybrán algoritmus VEGA pro implementaci v jazyce JAVA na předem zvolený problém. Tímto problémem je výběr sloupů kvádrového profilu podle předem stanovených kritérií. Vybraný algoritmus pracuje na principu rozdělení populace do několika skupin a každou takto vzniklou skupinu ohodnotí fitness funkcí. Následuje ukázka implementace tohoto algoritmu. Dále je zde ukázka práce s FrameWorkem. V další části jsou srovnány výsledky vytvořeného programu s výsledky, které byly získány pomocí FrameWorku WWW NIMBUS. Jak pro VEGA, tak pro NIMBUS jsou zde ukázány jednotlivé výsledky. U VEGA je předveden i vývoj jednotlivých fitness funkcí. Také jsou zde ukázány grafy, které je možné získat pomocí NIMBUS. Na konci je uvedeno porovnání výsledků a navržení možných vylepšení.

关键词： Evoluení algoritmy Genetické algoritmy multi-ktiteriální algoritmy FrameWork VEGA SPEA NSGA-II NIMBUS mutace křížení JAVA Eclipse. Evolutional algorithms Genetic algorithms multi-objective algorithms FrameWork VEGA SPEA NSGA-II NIMBUS

来源：评论

学校读者我要写书评

暂无评论

BPEL process defects prediction using multi-objective evolutionary search

引用

JOURNAL OF SYSTEMS AND SOFTWARE 2023年第1期204卷

作者： Daaji, Marwa Ouni, Ali Gammoudi, Mohamed Mohsen Bouktif, Salah Mkaouer, Mohamed Wiem Univ Tunis El Manar Fac Sci Tunis Tunisia Univ Quebec ETS Montreal Montreal PQ Canada Univ Manouba Higher Inst Multimedia Art Manouba Tunisia UAE Univ Dept Comp Sci & Software Engn CIT Al Ain U Arab Emirates Rochester Inst Technol Rochester NY USA

Web services are becoming increasingly popular technologies for modern organizations to improve their cooperation and collaboration through building new software systems by composing pre-built services. Such services are typically composed and executed through BPEL (Business Process Execution Language) processes. Like any other software artifact, such processes are frequently changed to add new or modify existing functionalities or adapt to environmental changes. However, poorly planned changes may introduce BPEL process design defects known as anti-patterns or defects. The presence of defects often leads to a regression in software quality. In this paper, we introduce an automated approach to predict the presence of defects in BPEL code using multi-objective Genetic Programming (MOGP). Our approach consists of learning from real-world instances of each service-based business process defect (i.e., anti-pattern) type to infer prediction rules based on the combinations of process metrics and their associated threshold values. We evaluate our approach based on a dataset of 178 real-world business processes that belong to various application domains, and a variety of BPEL process defect types such as data flow and portability defects. The statistical analysis of the achieved results shows the effectiveness of our approach in identifying defects compared with state-of-the-art techniques with a median accuracy of 91%.& COPY;2023 Elsevier Inc. All rights reserved.

关键词： BPEL process Anti-patterns multi-objective algorithms Genetic programming

来源：评论

学校读者我要写书评

暂无评论

An Enhanced multi-objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks

引用

IEEE ACCESS 2021年 9卷 149176-149195页

作者： Moshref, Mahmoud Al-Sayyed, Rizik Al-Sharaeh, Saleh Univ Jordan King Abdullah II Sch IT Dept Comp Sci Amman 11942 Jordan Univ Jordan King Abdullah II Sch IT Dept Informat Technol Amman 11942 Jordan

In recent years, Wireless Sensor Networks (WSNs) have benefitted from their integration with Internet of Things (IoT) applications. WSN usage for monitoring and tracing applications shows massive acceleration, whether indoors or outdoors. WSN is constructed from interconnected sensors, limited resource (battery), which requires considerable importance on deployment and routing strategies, to improve the performance of Quality of Service (QoS) in WSNs. Many of the existing strategies are based on metaheuristics algorithms such as Genetic algorithms to resolve the problem. This research proposes a new algorithm, Enhanced Non-Dominated Sorting Genetic Routing Algorithm (ENSGRA), to improve the QoS in WSNs. The proposed algorithm relies on Non-Dominated Sorting Genetic Algorithm 3 (NSGA-III), but adjusts reference points through the use of a dynamic weighted clustered scheduled vector to obtain new solutions. Moreover, ENSGRA can be used to find an integration between two parents crossover with multi-parent crossover (MPX), to produce multiple children and improve new offspring to obtain the optimal Pareto Fronts (PF). This algorithm excels when compared with the lagged multi-objective jumping particle swarm optimization, Non-dominated Sorting Genetic Algorithm-II and NSGA-III in terms of the QoS model (31% optimization percentage). Results show that the proposed ENSGRA is superior over other algorithms in evaluation measures for multi-objective algorithms.

关键词： Wireless sensor networks Sensors Quality of service Clustering algorithms Routing Sorting Heuristic algorithms Quality of service wireless sensor networks multi-objective algorithms clustering scheduling pareto front

来源：评论

学校读者我要写书评

暂无评论

An Enhanced multi-objective Particle Swarm Optimization in Water Distribution Systems Design

引用

WATER 2021年第10期13卷 1334页

作者： Torkomany, Mohamed R. Hassan, Hassan Shokry Shoukry, Amin Abdelrazek, Ahmed M. Elkholy, Mohamed Egypt Japan Univ Sci & Technol E JUST Environm Engn Dept Alexandria 21934 Egypt Alexandria Univ Irrigat Engn & Hydraul Dept Alexandria 11432 Egypt City Sci Res & Technol Applicat SRTA City Elect Mat Res Dept Adv Technol & New Mat Res Inst Alexandria 21934 Egypt E JUST Comp Sci & Engn Dept Alexandria 21934 Egypt Alexandria Univ Comp & Syst Engn Dept Alexandria 11432 Egypt

The scarcity of water resources nowadays lays stress on researchers to develop strategies aiming at making the best benefit of the currently available resources. One of these strategies is ensuring that reliable and near-optimum designs of water distribution systems (WDSs) are achieved. Designing WDSs is a discrete combinatorial NP-hard optimization problem, and its complexity increases when more objectives are added. Among the many existing evolutionary algorithms, a new hybrid fast-convergent multi-objective particle swarm optimization (MOPSO) algorithm is developed to increase the convergence and diversity rates of the resulted non-dominated solutions in terms of network capital cost and reliability using a minimized computational budget. Several strategies are introduced to the developed algorithm, which are self-adaptive PSO parameters, regeneration-on-collision, adaptive population size, and using hypervolume quality for selecting repository members. A local search method is also coupled to both the original MOPSO algorithm and the newly developed one. Both algorithms are applied to medium and large benchmark problems. The results of the new algorithm coupled with the local search are superior to that of the original algorithm in terms of different performance metrics in the medium-sized network. In contrast, the new algorithm without the local search performed better in the large network.

关键词： multi-objective algorithms network resilience particle swarm optimization water distribution systems

来源：评论

学校读者我要写书评

暂无评论

Distributed multi-objective evolutionary optimization using island-based selective operator application

引用

APPLIED SOFT COMPUTING 2019年 85卷 105757-000页

作者： Garcia-Sanchez, P. Ortega, J. Gonzalez, J. Castillo, P. A. Merelo, J. J. Univ Cadiz Dept Comp Sci & Engn ESI Cadiz Spain Univ Granada Dept Comp Architecture & Technol ETSIIT CITIC Granada Spain

Parallel island-model co-evolutionary algorithms are well-known methods, suitable for dealing with large multi-objective optimization problems. This paper proposes a version of these algorithms where each island modifies a fragment of the chromosome that encodes a possible solution to the problem. The objective of this paper is to demonstrate that automatically setting the size of the overlapping fragments depending on the number of islands obtains better results than using a fixed overlapping size. This method has been compared to other parallel evolutionary techniques considering a different number of islands, chromosome sizes and benchmarks. The analysis of the obtained experimental results, by using different metrics, shows that our approach can provide statistically significant improvements with respect to the base algorithm in high-dimensional, un-decomposable, multiobjective problems. This opens a very promising line to automatically adapt the overlapping sizes in this kind of algorithms. (C) 2019 Elsevier B.V. All rights reserved.

关键词： multi-objective algorithms NSGA-II Distributed evolutionary algorithms Island model

来源：评论

学校读者我要写书评

暂无评论

Approaching the bi-objective critical node detection problem with a smart initialization-based evolutionary algorithm

引用

PEERJ COMPUTER SCIENCE 2021年 7卷 e750页

作者： Beczi, Eliezer Gasko, Noemi Babes Bolyai Univ Cluj Napoca Cluj Napoca Romania

Determining the critical nodes in a complex network is an essential computation problem. Several variants of this problem have emerged due to its wide applicability in network analysis. In this article we study the bi-objective critical node detection problem (BOCNDP), which is a new variant of the well-known critical node detection problem, optimizing two objectives at the same time: maximizing the number of connected components and minimizing the variance of their cardinalities. Evolutionary multi-objective algorithms (EMOA) are a straightforward choice to solve this type of problem. We propose three different smart initialization strategies which can be incorporated into any EMOA. These initialization strategies take into account the basic properties of the networks. They are based on the highest degree, random walk (RW) and depth-first search. Numerical experiments were conducted on synthetic and real-world network data. The three different initialization types significantly improve the performance of the EMOA.

关键词： Complex networks Critical nodes Bi-objective critical node detection multi-objective algorithms Memetic algorithms

来源：评论

学校读者我要写书评

暂无评论

multiobjective evolutionary pruning of Deep Neural Networks with Transfer Learning for improving their performance and robustness

引用

APPLIED SOFT COMPUTING 2023年 147卷

作者： Poyatos, Javier Molina, Daniel Martinez-Seras, Aitor Del Ser, Javier Herrera, Francisco Univ Granada Andalusian Res Inst Data Sci & Computat Intelligen Dept Comp Sci & Artificial Intelligence Granada 18071 Spain TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Spain Univ Basque Country UPV EHU Bilbao 48013 Spain

Evolutionary Computation algorithms have been used to solve optimization problems in relation with architectural, hyper-parameter or training configuration, forging the field known today as Neural Architecture Search. These algorithms have been combined with other techniques such as the pruning of Neural Networks, which reduces the complexity of the network, and the Transfer Learning, which lets the import of knowledge from another problem related to the one at hand. The usage of several criteria to evaluate the quality of the evolutionary proposals is also a common case, in which the performance and complexity of the network are the most used criteria. This work proposes MO-EvoPruneDeepTL, a multi-objective evolutionary pruning algorithm. MO-EvoPruneDeepTL uses Transfer Learning to adapt the last layers of Deep Neural Networks, by replacing them with sparse layers evolved by a genetic algorithm, which guides the evolution based in the performance, complexity and robustness of the network, being the robustness a great quality indicator for the evolved models. We carry out different experiments with several datasets to assess the benefits of our proposal. Results show that our proposal achieves promising results in all the objectives, and direct relation are presented among them. The experiments also show that the most influential neurons help us explain which parts of the input images are the most relevant for the prediction of the pruned neural network. Lastly, by virtue of the diversity within the Pareto front of pruning patterns produced by the proposal, it is shown that an ensemble of differently pruned models improves the overall performance and robustness of the trained networks.(c) 2023 Published by Elsevier B.V.

关键词： Evolutionary Deep Learning multi-objective algorithms Pruning Out of Distribution detection Transfer Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：