Grouping Genetic Algorithm Research Articles

Near optimal citywide WiFi network deployment using a hybrid grouping genetic algorithm

This paper presents the application of a Hybrid Grouping Genetic Algorithm (HGGA) to solve the problem of deploying metropolitan wireless networks. In particular, the exploitation of the existing broadband infrastructure (e.g., ADSL networks) by “opening up” WiFi-enabled routers to third party users, is considered to produce a complex problem, henceforth call WiFi network Design Problem or WiFiDP. The application of a HGGA to this problem produces cost-effective network deployment plans, considering real life aspects such as budget (the total cost of deployment – i.e. the cost of opening all selected DSL routers for public use – should not exceed the allocated budget) and DSL router characteristics (coverage, DSL capacity at a specific location, unit price, etc.) The hybrid grouping genetic algorithm proposed incorporates a particular encoding to tackle the WiFiDP, in which the group part also includes the type of router to be installed. Also, a modification of this encoding to consider the working frequencies of routers is presented in this paper. Moreover, a repairing and local search procedures are added to the algorithm to obtain better performance and always find viable solutions. The performance and effectiveness of the proposed HGGA is evaluated using two randomly generated WiFiDP instances (considering 1000 and 2000 users), used to perform several experiments. The comparison of the proposed HGGA results against those of a greedy optimization algorithm (previously proposed to solve the WiFiDP) shows the better performance of this approach. Finally, the application of the HGGA to real datasets in the cities of Berlin (Germany) and Torrejón de Ardoz (Spain) is also reported in the experimental part. In real conditions, the HGGA keeps performing better than previous methods.

Are Microarray-Derived Gene Expression Signatures Useful as a Routine Assay for Prognostic Evaluation of Acute Myeloid Leukemia Samples with Low Leukemic Blast Content ?.

AbstractAbstract 1658Prognostic evaluation is a critical step in newly diagnosed pts with AML. With samples containing a high leukemic blast load, microarray-based gene expression profiling (GEP) and class prediction analyses have demonstrated their ability to assign AML samples to one of the three well characterized favorable-risk AML subtypes, ie, APL, CBFA-AMLs, CBFB-AMLs, with high accuracy and low error rates. One major issue regarding the use of microarray-based GEP in a routine prognostic workflow, concerns its ability to perform accurate class prediction analyses with samples containing as low as 20% blasts. So far, the minimum leukemic cell load that would still lead to correct class assignment has not been extensively studied. Another critical issue for a routine use of microarray-based GEP concerns samples that do not fulfill all quality control criteria along the process. The accuracy of class prediction analyses using data derived from poor quality AML samples also deserves further evaluations.In this study, using the Illumina BeadChip technology, GEP was first assessed for its ability to correctly classify APLs, CBFA-AMLs, CBFB-AMLs within a training set of 72 samples containing at least 60% of leukemic cells (20 APL samples from 14 pts, 12 CBFA-AML samples - n=6 pts, 11 CBFB-AML samples - n=10 pts). This training set also included normal bone marrow samples as well as a more heterogeneous group of 29 NK-AML samples (28 pts). The classifiers derived from this supervised analysis were then evaluated for their ability to assign to the correct class 93 test samples with either low leukemic blast load (as low as 5% of blasts – n=79 - 22 samples that originally contained less than 60% of leukemic cells, 57 samples with high blast load that were artificially diluted within a pool of normal bone marrows at 50 and 75%, ie, final leukemic blast load after dilution ranging from 10 to 50%), poor quality control criteria (n=10), or with atypical characteristics (n=4). The Class Prediction Module of ArrayMiner 5.3.3 software, which is a supervised method based on a proprietary optimization technique called grouping genetic algorithms (GGA), was used to build the classifiers associated with each class of the Training Set and assess the predictive capacity of these classifiers on Test Set samples.Using the Training Set and the GGA-base supervised method, the best model fitness was achieved with classifiers including 14 markers per class. With these classifiers, all Training Set samples were assigned to the correct class. Considering Test Set samples, all APL and CBFA-AML samples were assigned to the correct class, including the ones with the lowest leukemic blast load, i.e., 7% for APLs and 15% for CBFA-AMLs. One of the 23 CBFB-AML Test Set samples, which contained 5% of blasts was misclassified. All other CBFB-AML test samples were correctly classified, even those contained as low as 10% of leukemic cells. All but one of the NK-AML test samples were assigned to the correct class, even though some contained as low as 19% of leukemic cells. Concerning the 10 poor quality control samples characterized by either low RIN values and degraded total RNAs, low amounts of labeled cRNAs or both low RIN value and low labeled cRNA, all were assigned to the expected class. All atypical AMLs were assigned, as expected, to the NK-AML group.In conclusion, favorable cytogenetic risk AMLs with low leukemic cell load or poor quality control criteria can be correctly assign to the appropriate group using GEP. Transposability of this Illumina technology-based model and its efficiency in term of predictive capacity, will be presented using an independent series of 245 AML samples processed at a different institution, on Affymetrix GeneChips. Disclosures:No relevant conflicts of interest to declare.

Grouping Genetic Algorithm for the Blockmodel Problem

Many areas of research examine the relationships between objects. A subset of these research areas focuses on methods for creating groups whose members are similar based on some specific attribute(s). The blockmodel problem has as its objective to group objects in order to obtain a small number of large groups of similar nodes. In this paper, a grouping genetic algorithm (GGA) is applied to the blockmodel problem. Testing on numerous examples from the literature indicates a GGA is an appropriate tool for solving this type of problem. Specifically, our GGA provides good solutions, even to large-size problems, in reasonable computational time.

A differential evolution algorithm for the manufacturing cell formation problem using group based operators

Cellular manufacturing (CM) is an important application of group technology (GT), a manufacturing philosophy in which parts are grouped into part families, and machines are allocated into machine cells to take advantage of the similarities among parts in manufacturing. The target is to minimize inter-cellular movements. Inspired by the rational behind the so called grouping genetic algorithm (GGA), this paper proposes a grouping version of differential evolution (GDE) algorithm and its hybridized version with a local search algorithm (HGDE) to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 40 problem instances from literature and compare the performance of GGA and GDE. We also compare the performance of both algorithms when they are tailored with a local search algorithm. Our computations reveal that the proposed algorithm performs well on all test problems, exceeding or matching the best solution quality of the results presented in previous literature.

An Enhanced Grouping Genetic Algorithm for solving the cell formation problem

Cell formation is often the first step in solving facility layout design problems. The objective is to group part families and machines so that they can be assigned to manufacturing cells. The cell formation problem is a non-deterministic polynomial (NP) complete problem which means that the time taken to produce solutions increases exponentially with problem size. This paper presents the Enhanced Grouping Genetic Algorithm (EnGGA) that has been developed for solving the cell formation problem. The EnGGA replaces the replacement heuristic in a standard Grouping Genetic Algorithm with a Greedy Heuristic and employs a rank-based roulette–elitist strategy, which is a new mechanism for creating successive generations. The EnGGA was tested using well-known data sets from the literature. The quality of the solutions was compared with those produced by other methods using the grouping efficacy measure. The results show that the EnGGA is effective and outperforms or matches the other methods.

A GROUPING GENETIC ALGORITHM FOR THE MULTIPLE TRAVELING SALESPERSON PROBLEM

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.

A hybrid grouping genetic algorithm for the registration area planning problem

With the growing use of mobile communication devices, the management of such technologies is of increasing importance. The registration area planning (RAP) problem examines the grouping of cells comprising a personal communication services (PCS) network into contiguous blocks in an effort to reduce the cost of managing the location of the devices operating on the network, in terms of bandwidth. This study introduces a hybridized grouping genetic algorithm (HGGA) to obtain cell formations for the RAP problem. The hybridization is accomplished by adding a tabu search-based improvement operator to a traditional grouping genetic algorithm (GGA). Results indicate that significant performance gains can be realized by hybridizing the algorithm, especially for larger problem instances. The HGGA is shown to consistently outperform the traditional GGA on problems of size greater than 19 cells.

Modular design to support green life-cycle engineering

The severe competition in the market has driven enterprises to produce a wider variety of products to meet consumers’ needs. However, frequent variation of product specifications causes the assembly and disassembly of components and modules to become more and more complicated. As a result, the issue of product modular design is a problem worthy of concern. In this study, engineering attributes were added to the liaison graph model for the evaluation of part connections. The engineering attributes added, including contact type, combination type, tool type, and accessed direction, serve to offer designers criteria for evaluating the component liaison intensity during the design stage. A grouping genetic algorithm (GGA) is then employed for clustering the components and crossover mechanisms are modified according to the need of modular design. Furthermore, a reasonable green modular design evaluation is conducted using the green material cost analysis. According to the results, adjusted design proposals are suggested and materials that cause less pollution are recommended to replace the components with pollution values higher than those in the module. Finally, the authors use Borland C++ 6.0 to evaluate the system and clustering method. To illustrate the methodology proposed in this study, a table lamp is offered as an example.

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

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.

Handicapped Person Transportation: An application of the Grouping Genetic Algorithm

An effective method based on the Genetic Algorithms is proposed to solve the Handicapped Person Transportation problem, which is a real-life application for pickup and delivery problems. In these problems, vehicles have to transport (clients, loads, etc.,) from their locations to different destinations (hospitals, shop centres, etc.). The objective of this paper is to implement Grouping Genetic Algorithm to find optimal (or close to optimal) routes for transporting handicapped people in terms of service quality and number of used vehicles. This algorithm is a stochastic search method based on randomized operators for combining solutions and producing better ones. The proposed algorithm has been applied on the handicapped persons transportation problem in the city of Brussels, Belgium. The obtained results are better than the manually generated solutions in terms of service quality and computational effort.

Dynamic vehicle routing by means of a genetic algorithm

PurposeTo propose and to evaluate a new genetic algorithm (GA) for solving the dynamic pickup and delivery problem with time windows (DPDPTW).Design/methodology/approachFirst, a grouping genetic algorithm (GGA) for the (static) PDPTW is described. In order to solve the dynamic problem, the GGA then is embedded in a rolling horizon framework. Special updating mechanisms are provided which assure that reusable solution knowledge is preserved over the plan revisions. The approach is evaluated using a large number of test instances with varying degrees of dynamism.FindingsThe experimental results have demonstrated that the proposed approach is able to find high‐quality solutions when compared with two comparative heuristics.Research limitations/implicationsFuture research will be dedicated to the following issues: testing the proposed method using larger problem instances, using more sophisticated objective functions in order to further improve and evaluate the approach, integrating fast local search techniques into the genetic search, speeding up the algorithm by optimizing its implementation.Practical implicationsIn order to meet the increasing demands on the flexibility and the promptness of transportation services, algorithms are needed for dispatching transportation requests that arrive dynamically during the planning period. The findings of this contribution justify the employment of GAs in such dynamic transportation planning environments.Originality/valueAlthough the application of GAs in dynamic environments attracts growing attention, up to now no such algorithm has been published for the DPDPTW. To the best of the author's knowledge, this is the first time a GA has been applied to the DPDPTW.

A group genetic algorithm for the machine cell formation problem

The facility layout is a fundamental element to achieve the company's manufacturing strategy. To obtain a good layout design, appropriate computational algorithms as an aid to the layout designer in the decision making process during the factory layout design should be applied. This work presents a new genetic algorithm (GA) for the cell layout design. It is based on group encoding instead of simple machine encoding, generally used by most of the GA implementations encountered in the literature. Moreover, new crossover and mutation operators are proposed. Tests using published data show that the proposed algorithm can find the group structure present in the data sets.

A grouping genetic algorithm for the multi-objective cell formation problem

In this research, we propose an efficient method to solve the multi-objective cell formation problem (CFP) partially adopting Falkenauer's grouping genetic algorithm (GGA). The objectives are the minimization of both the cell load variation and intercell flows considering the machines’ capacities, part volumes and part processing times on the machines. We relax the cell size constraints and solve the CFP without predetermination of the number of cells, which is usually difficult to predict in a real-world CFP design. We also make some effort to improve the efficiency of our algorithm with respect to initialization of the population, fitness valuation, and keeping crossover operator from cloning. Numerical examples are tested and comparisons are made with general genetic algorithms (GAs). The result shows that our method is effective and flexible in both grouping machines into cells and deciding on the number of cells for the optimal solution.

A Grouping Genetic Algorithm for the Pickup and Delivery Problem with Time Windows

The Pickup and Delivery Problem with Time Windows (PDPTW) is a generalization of the well studied Vehicle Routing Problem with Time Windows (VRPTW). Since it models several typical planning situations in operational transportation logistics and public transit, the PDPTW has attracted growing interest in recent years. This paper proposes a Grouping Genetic Algorithm (GGA) for solving the PDPTW which features a group-oriented genetic encoding in which each gene represents a group of requests instead of a single request. The GGA is subject to a comparative test on the basis of two publicly available benchmark problem sets that comprise 9 and 56 PDPTW instances, respectively. The results show that the proposed GGA is competitive.

Evaluating performance advantages of grouping genetic algorithms

The genetic algorithm (GA) and a related procedure called the grouping genetic algorithm (GGA) are solution methodologies used to search for optimal solutions in constrained optimization problems. While the GA has been successfully applied to a range of problem types, the GGA was created specifically for problems involving the formation of groups. Falkenauer (JORBEL—Belg. J. Oper. Res. Stat. Comput. Sci. 33 (1992) 79), the originator of the GGA, and subsequent researchers have proposed reasons for expecting the GGA to perform more efficiently than the GA on grouping problems. Yet, there has been no research published to date which tests claims of GGA superiority. This paper describes empirical tests of the performance of GA and GGA in three domains which have substantial, practical importance, and which have been the subject of considerable academic research. Our purpose is not to determine which of these two approaches is better across an entire problem domain, but rather to begin to document practical differences between a standard off-the-shelf GA and a tailored GGA. Based on the level of solution quality desired, it may be the case that the additional time and resources required to design a tailored GGA may not be justified if the improvement in solution quality is only minor or non-existent.

PRODUCT FAMILY DESIGN WITH GROUPING GENETIC ALGORITHM—A CASE STUDY

ABSTRACT Product family is a result of derivation design, which is constructed to target various niche market segments. However, component variety may also cause over design and resources waste due to deficient preliminary analysis. Modular design to form product platform is, thus, important to enhance resource allocation and promote mass customization. In this study, a mathematic programming is proposed to simultaneously conduct platform and modular design as well as to optimize the number of modules. As to the issue of combinational components allocation, a heuristic grouping genetic algorithm is proposed, where crossover, mutation, and initial population operations is modified for such specific domain. Furthermore, a systematic design approach is developed according to product planning, functional relationships analysis, and physical relationships analysis, in which functional flow chart, interaction matrices, and computer programming are adopted to visualize the design process. Finally, a case design of coffee maker family is illustrated exhaustively.

Evolutionary algorithms for grouping high dimensional Email data

Grouping problems arise in many industrial and medical applications; examples include bin packing, workshop layout design, and graph colouring. This type of problem has been successfully handled using Grouping Genetic Algorithms. However in problems where there are perhaps thousands of objects to be grouped, we have found that Genetic Algorithm approaches can run into problems. This paper continues our research into a method we have developed for decomposing a large number of objects into mutually exclusive subsets where within-group dependencies are high and between-group dependencies are low. The method uses an Evolutionary Algorithm approach but where the whole population is a solution to the grouping problem rather than considering many candidate solutions. This reduces the resource overheads during computer implementation and the results are promising when compared with standard statistical methods and a Hill Climbing algorithm, all applied to email log file data.

Impact of the replacement heuristic in a grouping genetic algorithm

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 naı̈ve replacement heuristic (GGA 0) to a GGA that includes an intelligent replacement heuristic (GGA CF). We evaluate both the naı̈ve 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. Scope and purpose Our research investigates the significance of the replacement heuristic used as a part of the crossover operator in a grouping genetic algorithm (GGA). We test two GGAs and compare their replacement heuristics using test problems from the well-known machine-part cell formation domain. The purpose of our research is three-fold. First, we compare and contrast the GGA with standard GA to improve understanding of how they differ in problem representation and operation. Second, we provide evidence that GGA is limited not only to problems where the objective is to form groups, but also to problems where it is practical to incorporate a substantial amount of problem-specific information. Third, we estimate the impact that the GGA replacement heuristic has on performance. Results indicate that GGA performs up to 40% worse when problem-specific knowledge is not incorporated into the replacement heuristic.

CF-GGA: A grouping genetic algorithm for the cell formation problem

In manufacturing, the machine-part cell formation (MPCF) problem addresses the issues surrounding the formation of part families based on the processing requirements of the components, and the identification of machine groups based on their ability to process specific part families. Past research has shown that one key aspect of attaining efficient groupings of parts and machines is the block-diagonalization of the given machine-part (MP) incidence matrix. This paper presents and tests a grouping genetic algorithm (GGA) for solving the MPCF problem and gauges the quality of the GGA's solutions using the measurements of efficiency (Chandrasekharan and Rajagopalan 1986a) and efficacy (Kumar and Chandrasekharan 1990). The GGA in this study, CF-GGA, a grouping genetic algorithm for the cell formation problem, performs very well when applied to a variety of problems from the literature. With a minimal number of parameters and a straightforward encoding, CF-GGA is able to match solutions with several highly complex algorithms and heuristics that were previously employed to solve these problems.

An adaptive hybrid genetic algorithm for the three-matching problem

This paper presents a hybrid genetic algorithm (GA) with an adaptive application of genetic operators for solving the 3-matching problem (3MP), an NP-complete graph problem. In the 3MP, we search for the partition of a point set into minimal total cost triplets, where the cost of a triplet is the Euclidean length of the minimal spanning tree of the three points. The problem is a special case of grouping and facility location problems. One common problem with GA applied to hard combinatorial optimization, like the 3MP, is to incorporate problem-dependent local search operators into the GA efficiently in order to find high-quality solutions. Small instances of the problem can be solved exactly, but for large problems, we use local optimization. We introduce several general heuristic crossover and local hill-climbing operators, and apply adaptation to choose among them. Our GA combines these operators to form an effective problem solver. It is hybridized as it incorporates local search heuristics, and it is adaptive as the individual recombination/improvement operators are fired according to their online performance. Test results show that this approach gives approximately the same or even slightly better results than our previous, fine tuned GA without adaptation. It is better than a grouping GA for the partitioning considered. The adaptive combination of operators eliminates a large set of parameters, making the method more robust, and it presents a convenient way to build a hybrid problem solver.