An efficient hierarchical parallel genetic algorithm for graph coloring problem

Abbasian, Reza; Mouhoub, Malek

doi:10.1145/2001576.2001648

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…For queen10_10.col dataset, no expected chromatic number is stated. In our experiment we found this number to be 13, whereas this number found in [8] and [9] are 15 and 14, respectively. Thus, our MA outperforms the previous works for a very complex dataset.…”

Section: Discussionmentioning

confidence: 48%

“…For queen10_10.col dataset, no expected chromatic number is mentioned in [12]. For this dataset, the found chromatic number of [8] and [9] are 15 and 14, respectively. The work of [7] did not report any result for this dataset.…”

Section: Resultsmentioning

confidence: 99%

“…The algorithm is run several times for several decreasing values of k and the minimum possible k value is taken as the minimum chromatic number. Another work used hierarchical parallel genetic algorithm for GCP [8], where the authors extended genetic algorithm with genetic modification operator introduced by them. A guided genetic algorithm for GCP called MSPGCA is reported in [9], where the authors finetuned the initial chromosomes using a simple genetic algorithm and then the deterministic MSPGCA algorithm is run to dynamically reduce the chromatic number.…”

Section: Prior Workmentioning

confidence: 99%

See 2 more Smart Citations

Memetic Algorithm to Solve Graph Coloring Problem

Al¹,

Chowdhury²,

Farhat³

et al. 2013

IJCTE

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Abstract-Graph coloring problem (GCP) is of great interest to the researchers in the area of soft computing. To solve GCP, we present a memetic algorithm (MA) that uses classical crossover operation as the main variation operator and a deterministic local improvement technique. For the first time in the literature, we use binary encoded chromosomes for GCP, which makes both the crossover and the local improvement easier. In the traditional evolutionary algorithm (EA) for GCP, k-coloring is used and the EA is run repeatedly until the lowest possible k is reached. In our MA, we start with the theoretical upper bound of chromatic number (maximum out-degree + 1) and in the process of evolution some of the colors are made unused to dynamically reduce the number of color. Thus, the solution is found in a single run of the MA reducing the total execution time in comparison to running k-coloring for several times. We experiment with 23 datasets taken from standard DIMACS benchmark and compare the result with several recent works. For all but one dataset, we obtain the minimum chromatic number stated in the DIMACS benchmark. For the remaining one dataset (queen10_10.col), we obtain better solution than others.

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Section: Discussionmentioning

confidence: 48%

Section: Resultsmentioning

confidence: 99%

Section: Prior Workmentioning

confidence: 99%

See 1 more Smart Citation

Memetic Algorithm to Solve Graph Coloring Problem

Al¹,

Chowdhury²,

Farhat³

et al. 2013

IJCTE

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show abstract

“…Since parallel GAs are capable of constructing the solutions more efficiently [18,22], the future target of this research is to solve the problem of winner determination in combinatorial reverse auctions by using the parallel GAs. Another future direction is to consider some other non-price dimensions such as warranty, customer rating, and time of delivery.…”

Section: Discussionmentioning

confidence: 99%

Evolutionary Techniques for Reverse Auctions

Shil¹,

Sadaoui²,

Mouhoub³

2013

ICA

Self Cite

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Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.

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“…In order to improve the time efficiency of our branch and bound technique, we will investigate several variable and value ordering heuristics ) that allow the branch and bound to return the optimal solution in a better running time. We will also explore other techniques based on parallel genetic algorithms (Abbasian, 2011) and ant colony optimization (Mouhoub, 2006). While these techniques do not guarantee the best outcome, they are in general very efficient in terms of response time needed to reach the optimal (or near to optimal) solution.…”

Section: Discussionmentioning

confidence: 99%

A Preference-Aware Interactive System for Online Shopping

Alanazi¹,

Mouhoub²,

Mohammed³

2012

CIS

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Preference Elicitation is very important for online shopping interactive applications. The potential buyers usually have interest in some of the attributes of the product they want to purchase. While the current online shopping systems allow the users to provide some keywords and other information in order to ?lter and get only what they need, these latter feel that what they get does not necessarily meet their satisfaction. In this paper, we propose a new shopping system that enables the customers to express their needs when buying a product online. More precisely, the users are given the ability to provide their requirements and desires in a friendly and interactive way. The system will then provide a list of suggestions meeting the users’ requirements and maximizing their desires. Requirements and desires are managed, in a unique model, respectively as a set of hard constraints and preferences where these latter can be quantitative (numerical), qualitative (ordinal) or both. These constraints and preferences represent a constraint optimization problem where optimal solutions (best outcomes) are those satisfying the hard constraints and maximizing the user’s preferences. The branch and bound method is applied in order to provide the user with a list of best outcomes.

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An efficient hierarchical parallel genetic algorithm for graph coloring problem

Cited by 20 publications

References 14 publications

Memetic Algorithm to Solve Graph Coloring Problem

Memetic Algorithm to Solve Graph Coloring Problem

Evolutionary Techniques for Reverse Auctions

A Preference-Aware Interactive System for Online Shopping

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