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Impact of the replacement heuristic in a grouping genetic algorithm

COMPUTERS & OPERATIONS RESEARCH 2003年第11期30卷 1575-1593页

作者： Brown, EC Sumichrast, RT Virginia Tech Blacksburg VA 24061 USA Virginia Tech RB Pamplin Coll Business Blacksburg VA 24061 USA

The grouping genetic algorithm (GGA), developed by Emmanuel Falkenauer, is a genetic algorithm whose encoding and operators are tailored to suit the special structure of grouping problems. In particular, the crossover operator for a GGA involves the development of heuristic procedures to restore group membership to any entities that may have been displaced by preceding actions of the operator. In this paper, we present evidence that the success of a GGA is heavily dependent on the replacement heuristic used as a part of the crossover operator. We demonstrate this by comparing the performance of a GGA that uses a naive replacement heuristic (GGA(0)) to a GGA that includes an intelligent replacement heuristic (GGA(CF)). We evaluate both the naive and intelligent approaches by applying each of the two GGAs to a well-known grouping problem, the machine-part cell formation problem. The algorithms are tested on problems from the literature as well as randomly generated problems. Using two measures of effectiveness, grouping efficiency and grouping efficacy, our tests demonstrate that adding intelligence to the replacement heuristic enhances the performance of a GGA, particularly on the larger problems tested. Since the intelligence of the replacement heuristic is highly dependent on the particular grouping problem being solved, our research brings into question the robustness of the GGA.

关键词： grouping genetic algorithm heuristic

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An Effective Approach for Obtaining a Group Trading Strategy Portfolio Using grouping genetic algorithm

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IEEE ACCESS 2019年 7卷 7313-7325页

作者： Chen, Chun-Hao Chen, Yu-Hsuan Lin, Jerry Chun-Wei Wu, Mu-En Tamkang Univ Dept Comp Sci & Informat Engn Taipei 25137 Taiwan Western Norway Univ Appl Sci Dept Comp Math & Phys N-5063 Bergen Norway Natl Taipei Univ Technol Dept Informat & Finance Management Taipei 10608 Taiwan

To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental indicators. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy, and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. genetic operators, including crossover, mutation, and inversion, are applied on the population to form a new offspring. Evolution is continued until the stop conditions are reached. Lastly, experiments were conducted on two real datasets with different trends to show that the advantages and the effectiveness of the presented approach.

关键词： Group trading strategy portfolio grouping genetic algorithm portfolio optimization trading strategy trading strategy portfolio

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A Series-based group stock portfolio optimization approach using the grouping genetic algorithm with symbolic aggregate Approximations

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KNOWLEDGE-BASED SYSTEMS 2017年第Jun.1期125卷 146-163页

作者： Chen, Chun-Hao Yu, Chih-Hung Tamkang Univ Dept Comp Sci & Informat Engn Taipei Taiwan

Stock portfolio optimization is both an attractive research topic and a complex problem due to the rapidly changing economy. Based on optimization techniques, many algorithms have been proposed to mine different portfolios. In the previous approach, a group stock portfolio (GSP) was derived based on the investors' objective and subjective requests by the grouping genetic algorithm. Stocks were divided into groups, with those in the same group being similar. The benefit of using a GSP is that investors can replace any stock that they do not like with substitute stocks in the same group. To increase the similarity of stocks in groups, stock price series are taken into consideration, and an enhanced approach is proposed to derive a series-based GSP that can be used to provide more actionable stock portfolios to investors making decisions. In chromosome representation, grouping, stock and stock portfolio parts are used to represent a GSP as did the previous approach. To increase the return and similarity of a GSP, the stability factor is designed based on cash dividends, and the unit and price balances are utilized as well. Because the dimension of stock price series is high, the symbolic aggregate approximation (SAX) and extended symbolic aggregate approximation (ESAX) are selected to transform data points into symbols. Then, the series distance factor is presented to evaluate the similarity of stock price series in groups of a GSP. By using the new factors and the existing factors in the previous approach, two new fitness functions are developed to evaluate the quality of chromosomes. Experiments on a real-world dataset were conducted to show the merits of the proposed approach using the two fitness functions with SAX and ESAX. The results show that the return on investment (ROI) of the proposed approach using the fitness functions with SAX is approximately 16% to 18% and better than the ROI obtained with ESAX. However, the proposed approach with ESAX achieves better

关键词： Extended symbolic aggregate approximation grouping genetic algorithm Group stock portfolio Symbolic aggregate approximation Stock price series

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A grouping genetic algorithm for the multiple traveling salesperson problem

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2007年第2期6卷 333-347页

作者： Brown, Evelyn C. Ragsdale, Cliff T. Carter, Arthur E. E Carolina Univ Dept Engn Greenville NC 27858 USA Virginia Tech Dept Business Informat Technol Blacksburg VA 24061 USA Radford Univ Coll Informat Sci & Technol Radford VA 24142 USA

The multiple traveling salesperson problem (MTSP) involves scheduling m > 1 salespersons to visit a set of n > m locations. Thus, the n locations must be divided into m groups and arranged so that each salesperson has an ordered set of cities to visit. The grouping genetic algorithm (GGA) is a type of genetic algorithm (GA) designed particularly for grouping problems. It has been successfully applied to a variety of grouping problems. This paper focuses on the application of a GGA to solve the MTSP. Our GGA introduces a new chromosome representation to indicate which salesperson is assigned to each tour and the ordering of the cities within each tour. We compare our method to standard GAs that employ either the one- chromosome or two-chromosome representation for MTSP. This research demonstrates that our GGA with its new chromosome representation is capable of solving a variety of MTSP problems from the literature and can outperform the traditional encodings of previously published GA methods.

关键词： multiple traveling salesperson problem grouping genetic algorithm genetic algorithm

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Minimising material handling cost in cell formation with alternative processing routes by grouping genetic algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2006年第11期44卷 2133-2167页

作者： Hu, L. Yasuda, K. Tohoku Univ Grad Sch Econ & Management Aoba Ku Sendai Miyagi 9808576 Japan

The alternative processing route is one of the important design factors for the cell formation problem (CFP) in cellular manufacturing systems (CMSs). genetic algorithm (GA) is a popular method for solving the CFPs, because GA is capable of searching large regions of the solution's space while being less susceptible to getting trapped in local optima. However, the disadvantage of classical GAs is that the number of manufacturing cells should be known in advance. Knowing the actual number of manufacturing cells is relatively difficult before the CMS design is determined. grouping genetic algorithm (GGA) is capable of solving CFP without predetermination of the number of cells, which is introduced by Falkenauer's GGA ( 1998). In order to adopt the GGA on CFP with alternative processing routes, we develop a new chromosome representation, a local optimisation algorithm for crossover operator and special mutation operators. These efforts ensure the efficiency of our method and are indicated in the numerical examples, and improved solutions are also obtained in the numerical examples.

关键词： cellular manufacturing cell formation alternative processing routes grouping genetic algorithm

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A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

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JOURNAL OF INTELLIGENT MANUFACTURING 2005年第2期16卷 189-205页

作者： Vin, E De Lit, P Delchambre, A Free Univ Brussels CAD CAM Dept B-1050 Brussels Belgium

This paper addresses the cell formation problem with alternative part routings, considering machine capacity constraints. Given processes, machine capacities and quantities of parts to produce, the problem consists in de. ning the preferential routing for each part optimising the grouping of machines into manufacturing cells. The main objective is to minimise the intercellular traffic, while respecting machine capacity constraints. To solve this problem, the authors propose an integrated approach based on a multiple-objective grouping genetic algorithm for the preferential routing selection of each part (by solving an associated resource planning problem) and an integrated heuristic for the cell formation problem.

关键词： cell formation group technology alternative routings grouping genetic algorithm multiple objectives

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A Novel grouping genetic algorithm for Assigning Resources to Users in WCDMA Networks 18

A Novel Grouping Genetic Algorithm for Assigning Resources t...

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18th European Conference on Applications of Evolutionary Computation (EvoApplications) as part of EvoSTAR Conference

作者： Cuadra, L. Salcedo-Sanz, S. Carnicer, A. D. Del Arco, M. A. Portilla-Figueras, J. A. Univ Alcala Dept Signal Proc & Communicat Madrid 28871 Spain

ISBN: (纸本)9783319165486;9783319165493

In this work we explore the feasibility of applying a novel grouping genetic algorithm (GGA) to the problem of assigning resources to mobile terminals or users in Wideband Code Division Multiple Access (WCDMA) mobile networks. In particular, we propose: (1) A novel cost function (to be minimized) that contains, in addition to the common load factors, other utilization ratios for aggregate capacity, codes, power, and users without service. (2) A novel encoding scheme, and modifications for the crossover and mutation operators, tailored for resource assignment in WCDMA networks. The experimental work points out that our GGA approach exhibits a superior performance than that of the conventional method (which minimizes only the load factors), since all users receive the demanded service along with a minimum use of the assigned resources (aggregate capacity, power, and codes).

关键词： grouping genetic algorithm WCDMA mobile networks Telecommunication

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A multiple-objective grouping genetic algorithm for the cell formation problem with alternative routings

A multiple-objective grouping genetic algorithm for the cell...

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International Conference on Production Systems Design Supply Chain Management and Logistics

作者： Vin, E De Lit, P Delchambre, A Free Univ Brussels CAD CAM Dept B-1050 Brussels Belgium

关键词： cell formation group technology alternative routings grouping genetic algorithm multiple objectives

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A new grouping genetic algorithm for the quadratic multiple knapsack problem

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7th European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP 2007)

作者： Singh, Alok Baghel, Anurag Singh Univ Allahabad Fac Sci JK Inst Appl Phys & Technol Allahabad 211002 Uttar Pradesh India Dept Elect Commun Rajasthan 302001 India

ISBN: (纸本)9783540716143

The quadratic multiple knapsack problem is an extension of the well known 0/1 multiple knapsack problem. In the quadratic multiple knapsack problem, profit values are associated not only with individual objects but also with pairs of objects. Profit value associated with a pair of objects is added to the overall profit if both objects of the pair belong to the same knapsack. Being an extension of the 0/1 multiple knapsack problem, this problem is also NP-Hard. In this paper, we have proposed a new steady-state grouping genetic algorithm for the quadratic multiple knapsack problem and compared our results with two recently proposed methods - a genetic algorithm and a stochastic hill climber. The results show the effectiveness of our approach.

关键词： combinatorial optimization grouping genetic algorithm knapsack problem quadratic multiple knapsack problem

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HGGASA: An annealing grouping genetic algorithm for finding feasible timetables

HGGASA: An annealing grouping Genetic Algorithm for finding ...

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4th International Conference on Innovations in Information Technology

作者： Najafi-Ardabili, Amir Qarouni-Fard, Danial Andalibizadeh, Mohamad-reza Ghorbani, Ooldooz Sheikhaei, Mohammad-Sadegh Mohammadzadeh, Javad Ferdowsi Univ Mashhad Dept Comp Sci Mashhad Iran Shahid Beheshti Univ ECE Fac Tehran Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran Univ Tehran Dept Comp Sci Tehran Iran

ISBN: (纸本)9781424418404

Timetabling is a well-known NP-Complete Constraint Satisfaction problem (CSP) that has been widely studied in the past. In this paper we adopt a modified genetic algorithm, better know as grouping GA and tweaked to suit grouping problems. GGA is further combined with Simulated Annealing (HGGASA) to implement the notion of an acceptance function and improve the performance rate of the algorithm. The results demonstrate a better convergence rate for HGGASA, but not uniformly.

关键词： timetable grouping genetic algorithm simulated annealing soft computing

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