检索结果-内蒙古大学图书馆

biased random-key genetic algorithms: A review

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第1期321卷 1-22页

作者： Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Resende, Mauricio G. C. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rua Marques de Sao Vicente 225 BR-22453900 Rio de Janeiro RJ Brazil AT&T Labs Res 200 South Laurel Ave Middletown NJ 07748 USA Univ Washington Ind & Syst Engn 3900 E Stevens Way NE Seattle WA 98195 USA

This paper is a comprehensive literature review of biased random-key genetic algorithms (BRKGA). BRKGA is a metaheuristic that employs random-key-based chromosomes with biased, uniform, and elitist mating strategies in a genetic algorithm framework. The review encompasses over 150 papers with a wide range of applications, including classical combinatorial optimization problems, real-world industrial use cases, and non-orthodox applications such as neural network hyperparameter tuning in machine learning. Scheduling is by far the most prevalent application area in this review, followed by network design and location problems. The most frequent hybridization method employed is local search, and new features aim to increase population diversity. We also detail challenges and future directions for this method. Overall, this survey provides a comprehensive overview of the BRKGA metaheuristic and its applications and highlights important areas for future research.

关键词： biased random-key genetic algorithms Literature review Metaheuristics Applications

来源：评论

学校读者我要写书评

暂无评论

biased random-key genetic algorithms for theWinner Determination Problem in Combinatorial Auctions

引用

EVOLUTIONARY COMPUTATION 2015年第2期23卷 279-307页

作者： de Andrade, Carlos Eduardo Toso, Rodrigo Franco Resende, Mauricio G. C. Miyazawa, Flavio Keidi Univ Estadual Campinas Inst Comp BR-13083852 Campinas SP Brazil Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA AT&T Labs Res Network Evolut Res Dept Middletown NJ 07748 USA

In this paper we address the problem of picking a subset of bids in a general combinatorial auction so as to maximize the overall profit using the first-pricemodel. This winner determination problem assumes that a single bidding round is held to determine both the winners and prices to be paid. We introduce six variants of biased random-key genetic algorithms for this problem. Three of them use a novel initialization technique thatmakes use of solutions of intermediate linear programming relaxations of an exact mixed integer linear programming model as initial chromosomes of the population. An experimental evaluation compares the effectiveness of the proposed algorithms with the standard mixed linear integer programming formulation, a specialized exact algorithm, and the best-performing heuristics proposed for this problem. The proposed algorithms are competitive and offer strong results, mainly for large-scale auctions.

关键词： Combinatorial auctions winner determination problem genetic algorithms biased random-key genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

biased random-key genetic algorithms for combinatorial optimization

引用

JOURNAL OF HEURISTICS 2011年第5期17卷 487-525页

作者： Goncalves, Jose Fernando Resende, Mauricio G. C. AT&T Labs Res Algorithms & Optimizat Res Dept Florham Pk NJ USA Univ Porto Fac Econ Porto LIAAD P-4100 Oporto Portugal

random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154-160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

关键词： genetic algorithms biased random-key genetic algorithms random-key genetic algorithms Combinatorial optimization Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

biased random-key genetic algorithms: A tutorial with applications 24

Biased random-key genetic algorithms: A tutorial with applic...

引用

8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence (ISMSI)

作者： Noronha, Thiago F. Ribeiro, Celso C. Univ Fed Minas Gerais BR-31270901 Belo Horizonte Brazil Univ Fed Fluminense BR-24210346 Niteroi Brazil Univ Fed Bahia BR-24210346 Niteroi Brazil

ISBN: (纸本)9798400717291

This tutorial overviews biased random-key genetic algorithms and their applications. They are characterized by their solution encoding and population evolution, with solutions represented by a vector of real numbers. Selection and crossover are biased to produce better than purely random offspring. We discuss the population evolution dynamics, design and implementation issues, and present a software framework. Extensions and improvements of the basic algorithm are discussed, followed by a review of applications.

关键词： genetic algorithms biased random-key genetic algorithms metaheuristics combinatorial optimization applications framework

来源：评论

学校读者我要写书评

暂无评论

Early years of biased random-key genetic algorithms: a systematic review

引用

JOURNAL OF GLOBAL OPTIMIZATION 2024年 1-24页

作者： Londe, Mariana A. Pessoa, Luciana S. Andrade, Carlos E. Resende, Mauricio G. C. Pontificia Univ Catolica Rio de Janeiro Dept Ind Engn Rua Marques Sao Vicente 225Gavea BR-22453900 Rio de Janeiro RJ Brazil AT&T Labs Res AT&T 200 South Laurel Ave Middletown NJ 07748 USA Univ Washington Ind & Syst Engn 3900 E Stevens Way NE Seattle WA 98195 USA

This paper presents a systematic literature review and bibliometric analysis focusing on biased random-key genetic algorithms (BRKGA). BRKGA is a metaheuristic framework that uses random-key-based chromosomes with biased, uniform, and elitist mating strategies alongside a genetic algorithm. This review encompasses around 250 papers, covering a diverse array of applications ranging from classical combinatorial optimization problems to real-world industrial scenarios, and even non-traditional applications like hyperparameter tuning in machine learning and scenario generation for two-stage problems. In summary, this study offers a comprehensive examination of the BRKGA metaheuristic and its various applications, shedding light on key areas for future research.

关键词： biased random-key genetic algorithms Bibliometric analysis Systematic literature review Metaheuristics

来源：评论

学校读者我要写书评

暂无评论

A C plus plus application programming interface for biased random-key genetic algorithms

引用

OPTIMIZATION METHODS & SOFTWARE 2015年第1期30卷 81-93页

作者： Toso, R. F. Resende, M. G. C. Rutgers State Univ Dept Comp Sci Piscataway NJ 08854 USA AT&T Labs Res Middletown NJ 07748 USA

In this paper, we describe brkgaAPI, an efficient and easy-to-use object-oriented application programming interface for the algorithmic framework of biased random-key genetic algorithms. Our cross-platform library automatically handles the large portion of problem-independent modules that are part of the framework, including population management and evolutionary dynamics, leaving to the user the task of implementing a problem-dependent procedure to convert a vector of random keys into a solution to the underlying optimization problem. Our implementation is written in the C++programming language and may benefit from shared-memory parallelism when available.

关键词： biased random-key genetic algorithms genetic algorithms application programming interface combinatorial optimization parallel programming

来源：评论

学校读者我要写书评

暂无评论

A C++application programming interface for biased random-key genetic algorithms

引用

Optimization Methods & Software 2015年第1期30卷 81-93页

作者： Resende, MGC Toso, R F Department of Computer Science Rutgers University Piscataway NJ 08854 USA

关键词： biased random-key genetic algorithms genetic algorithms application programming interface combinatorial optimization parallel programming 68N01 68R01 68T20 68U01 68W10 68W20

来源：评论

学校读者我要写书评

暂无评论

A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

引用

JOURNAL OF HEURISTICS 2017年第6期23卷 533-550页

作者： de Faria, H., Jr. Resende, M. G. C. Ernst, D. UFABC Univ Fed ABC CECS Rua Santa Adelia 166 BR-09210170 Santo Andre SP Brazil Univ Liege Inst Montefiore Quartier Polytech 110Alee Decouverte 4000B28 B-4000 Liege Belgium Amazon Com Math Optimizat & Planning 333 Boren Ave N Seattle WA 98109 USA

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

关键词： Distribution network reconfiguration biased random-key genetic algorithms Optimization Power losses

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for scheduling heterogeneous multi-round systems

引用

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2017年第5期24卷 1061-1077页

作者： Brandao, Julliany S. Noronha, Thiago F. Resende, Mauricio G. C. Ribeiro, Celso C. Univ Fed Fluminense Inst Comp BR-24210346 Niteroi RJ Brazil Ctr Fed Educ Tecnol Celso Suckow da Fonseca BR-20271110 Rio De Janeiro RJ Brazil Univ Fed Minas Gerais Dept Comp Sci BR-24105 Belo Horizonte MG Brazil Amazon Com Math Optimizat & Planning Seattle WA 98109 USA

A divisible load is an amount W of computational work that can be arbitrarily divided into independent chunks of load. In many divisible load applications, the load can be parallelized in a master-worker fashion, where the master distributes the load among a set P of worker processors to be processed in parallel. The master can only send load to one worker at a time, and the transmission can be done in a single round or in multiple rounds. The multi-round divisible load scheduling problem consists in (a) selecting the subset A. P of workers that will process the load, (b) defining the order in which load will be transmitted to each of them, (c) defining the number m of transmission rounds that will be used, and (d) deciding the amount of load that will be transmitted to each worker i. A at each round k is an element of {1, ..., m}, so as to minimize the makespan. We propose a heuristic approach that determines the transmission order, the set of the active processors and the number of rounds by a biased random-key genetic algorithm. The amount of load transmitted to each worker is computed in polynomial time by closed-form formulas. Computational results showed that the proposed genetic algorithm outperformed a closed-form state-of-the-art heuristic, obtaining makespans that are 11.68% smaller on average for a set of benchmark problems.

关键词： divisible loads divisible load scheduling multi-round biased random-key genetic algorithms metaheuristics

来源：评论

学校读者我要写书评

暂无评论

A biased random-key genetic algorithm for the unequal area facility layout problem

引用

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2015年第1期246卷 86-107页

作者： Goncalves, Jose Fernando Resende, Mauricio G. C. Univ Porto Fac Econ INESC TEC LIAAD P-4200464 Oporto Portugal Amazon Com Math Optimizat & Planning Seattle WA 98109 USA

This paper presents a biased random-key genetic algorithm (BRKGA) for the unequal area facility layout problem (UA-FLP) where a set of rectangular facilities with given area requirements has to be placed, without overlapping, on a rectangular floor space. The objective is to find the location and the dimensions of the facilities such that the sum of the weighted distances between the centroids of the facilities is minimized. A hybrid approach combining a BRKGA, to determine the order of placement and the dimensions of each facility, a novel placement strategy, to position each facility, and a linear programming model, to fine-tune the solutions, is developed. The proposed approach is tested on 100 random datasets and 28 of benchmark datasets taken from the literature and compared with 21 other benchmark approaches. The quality of the approach was validated by the improvement of the best known solutions for 19 of the 28 extensively studied benchmark datasets. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： Facilities planning and design Facility layout biased random-key genetic algorithms random-keys

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：