A grouping genetic algorithm with controlled gene transmission for the bin packing problem

Quiróz-Castellanos, Marcela; Cruz-Reyes, Laura; Torres-Jiménez, José; Gómez, S Claudia; Huacuja, Héctor Joaquín Fraire; Alvim, Adriana C. F.

doi:10.1016/j.cor.2014.10.010

Cited by 92 publications

(51 citation statements)

References 32 publications

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“…Fleszar and Charalambous reported that their Perturbation-SAWMBS method could not solve to optimality more instances in the hard28 dataset than the First Fit Decrease procedure (5 out of the 28), even after drastically increasing the maximum number of iterations in their algorithm. The same applies to the HI_BP algorithm, as reported in Quiroz-Castellanos et al (2015). In fact, most heuristics proposed for the bin packing problem, including the best performers, cannot solve to optimality more than 5 instances from this class.…”

Section: Bppmentioning

confidence: 61%

“…The authors also reported signicantly lower quality results for the WA heuristic compared to those given in Loh et al (2008). To the best of our knowledge, the most recent work in this area, is reported in Quiroz-Castellanos et al (2015). It involves a grouping genetic algorithm (GGA-CGT) that outperforms all previous algorithms with regard to the number of optimal solutions found, particularly for the most dicult set of instances hard28.…”

Section: Introductionmentioning

confidence: 93%

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Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing

Buljubasic

Vasquez

2016

Computers & Operations Research

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Section: Bppmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 93%

Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing

Buljubasic

Vasquez

2016

Computers & Operations Research

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“…Lim et al [31] borrowed from social monogamy: pair bonding and infidelity at a low probability and explored a pair of genetic algorithms to solve the 0-1 knapsack problem, and achieved better results. Quiroz-Castellanos et al [32] proposed a method that was referred to the grouping genetic algorithm with controlled gene transmission to solve the bin packing problem.…”

Section: Application Of a Genetic Algorithm In Combinatorial Optimizamentioning

confidence: 99%

Data Source Selection Based on an Improved Greedy Genetic Algorithm

Yang

Xing

2019

Symmetry

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The development of information technology has led to a sharp increase in data volume. The tremendous amount of data has become a strategic capital that allows businesses to derive superior market intelligence or improve existing operations. People expect to consolidate and utilize data as much as possible. However, too much data will bring huge integration cost, such as the cost of purchasing and cleaning. Therefore, under the context of limited resources, obtaining more data integration value is our expectation. In addition, the uneven quality of data sources make the multi-source selection task more difficult, and low-quality data sources can seriously affect integration results without the desired quality gain. In this paper, we have studied how to balance data gain and cost in the source selection, specifically, maximizing the gain of data on the premise of a given budget. We proposed an improved greedy genetic algorithm (IGGA) to solve the problem of source selection, and carried out a wide range of experimental evaluations on the real and synthetic dataset. The empirical results show considerable performance in favor of the proposed algorithm in terms of solution quality.

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“…The literature on these two subproblems is extensive, and we refer to Malaguti and Toth (2010), Lewis et al (2012), Quiroz-Castellanos et al (2015), Delorme et al (2016) and Lewis (2016) for some detailed reviews. Some of the current best metaheuristics for the bin packing problem include the grouping genetic algorithm of Falkenauer (1996), the extension of the Perturbation-MBS method by Fleszar and Charalambous (2011), and the grouping genetic algorithm with controlled gene transmission of Quiroz-Castellanos et al (2015). Population-based heuristics tend to be very frequently used, due to their capability of exploring very diverse areas of the search space.…”

Section: Introductionmentioning

confidence: 99%

A study on exponential-size neighborhoods for the bin packing problem with conflicts

et al. 2018

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We propose an iterated local search based on several classes of local and large neighborhoods for the bin packing problem with conflicts. This problem, which combines the characteristics of both bin packing and vertex coloring, arises in various application contexts such as logistics and transportation, timetabling, and resource allocation for cloud computing. We introduce O(1) evaluation procedures for classical local-search moves, polynomial variants of ejection chains and assignment neighborhoods, an adaptive set covering-based neighborhood, and finally a controlled use of 0-cost moves to further diversify the search. The overall method produces solutions of good quality on the classical benchmark instances and scales very well with an increase of problem size. Extensive computational experiments are conducted to measure the respective contribution of each proposed neighborhood. In particular, the 0-cost moves and the large neighborhood based on set covering contribute very significantly to the search. Several research perspectives are open in relation to possible hybridizations with other state-of-the-art mathematical programming heuristics for this problem.

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A grouping genetic algorithm with controlled gene transmission for the bin packing problem

Cited by 92 publications

References 32 publications

Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing

Consistent neighborhood search for one-dimensional bin packing and two-dimensional vector packing

Data Source Selection Based on an Improved Greedy Genetic Algorithm

A study on exponential-size neighborhoods for the bin packing problem with conflicts

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