Genetic algorithm for sex-fair stable marriage problem

Nakamura, Morikazu; Onaga, Kenji; Kyan, Seiki; Silva, M.

doi:10.1109/iscas.1995.521562

Cited by 11 publications

(7 citation statements)

References 2 publications

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“…Aldershof and Carducci [1] report optimistically on application of a genetic algorithm to two-sided matching problems, but the problems they examine are smaller than the 20×20 problems discussed here, so they do no address scaling issues. In [17] a genetic algorithm is used to find gender-unbiased solutions for the stable marriage problem. [5] has useful and suggestive findings pertaining to multiobjective evolutionary algorithms generally, as do [6] and [4].…”

Section: Related Workmentioning

confidence: 99%

On heuristics for two-sided matching

Kimbrough

Kuo

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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The stable marriage problem is prototypical of two-sided matching problems, widely encountered in practice, in which agents having preferences, interests and capacities for action of their own are paired up or matched. Standardly, variants of the well-known Gale-Shapley deferred acceptance algorithm (GS/DAA) are used to find stable matches. Using evolutionary computation and an agent-based model heuristics, this paper investigates the stable marriage problem as a multiobjective problem, looking at social welfare and equity or fairness, in addition to stability as important aspects of any proposed match. The paper finds that these heuristics are reliably able to discover matches that are Pareto superior to those found by the GS/DAA procedure. Ramifications of this finding are briefly explored, including the question of whether stability in a matching is often strictly required.

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Section: Related Workmentioning

confidence: 99%

On heuristics for two-sided matching

Kimbrough

Kuo

2010

Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation

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“…For example: Two parents (1,2,3,4,5,6,7,8,9,10) and (7,3,1,4,5,2,10,9,6,8), and the chosen part is (4,5,6,7), the resulting offspring is (3,1,4,5,6,7,2,10,9,8). As required, the offspring bears a structural relationship to both parents.…”

Section: Crossover Operator and Mutation Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Some works have also been reported for finding stable matchings [3,8] using GA. In [3] the author transforms the sex-fair stable marriage problem into a graph problem which is suitable for GA solution, and proposes a genetic algorithm to find it. In [4] an algorithm is proposed to find an egalitarian stable matching which minimizes the sum of the partner's preference rank in each person's PL.…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Fitness Functions for Stable Marriage Problem using Genetic Algrithm

Vien

Chung

2006

2006 SICE-ICASE International Joint Conference

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In this paper we propose a genetic algorithm (GA)-based approach to find out stable matchings in the stable marriage problem depending on different criteria such as stable matching with man-optimal, woman-optimal, egalitarian and sex-fair. The stable marriage problem is an extensively-studied combinatorial problem with many practical applications. Gale-Shapley (GS) algorithm is well known by which the stable matching found is extremal among many (for the worst case, in exponential order) stable matchings. So that Gale-Shapley algorithm can only search the man-optimal (or woman-pessimal) stable matching. The proposed algorithm has been evaluated by simulation experiment compared to other existing algorithms. It has been found that the proposed algorithm is quite efficient in finding stable matchings depending on different criteria.

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“…They also offer the flexibility to include multiple objectives when calculating allocations. Heuristic approaches have been used for matching problems before, e.g., GAs for strict (Kimbrough and Kuo 2010) and incomplete preferences (Haas 2014), and GAs to find fair solutions (Nakamura et al 1995). In contrast to previous work in this field, this article specifically considers the case of non-strict, not necessarily complete preferences.…”

Section: Introductionmentioning

confidence: 99%

Two-Sided Matching with Indifferences: Using Heuristics to Improve Properties of Stable Matchings

Haas

2020

Comput Econ

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Two-Sided Matching is a widely used approach to allocate resources based on preferences. In Two-Sided Matching problems where indifferences are allowed in the preference lists, finding stable matchings with certain properties is known to be NP-hard and, in some instances, even NP-hard to approximate. This article, therefore, considers the use of heuristics in Two-Sided Matching scenarios with indifferences to find and explore potential solutions and their properties. Two heuristics, a Genetic Algorithm and Threshold Accepting, are compared to existing approaches with respect to their ability to generate alternative stable solutions based on initially stable matchings for scenarios with either complete or incomplete preferences. The evaluation shows that using these types of heuristics is a valid approach to obtain matchings with improved properties such as fairness or average matched rank, compared to existing algorithms. Overall, this approach allows for the exploration of alternative stable matchings and subsequent selection of a best solution given selected evaluation criteria.

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Genetic algorithm for sex-fair stable marriage problem

Cited by 11 publications

References 2 publications

On heuristics for two-sided matching

On heuristics for two-sided matching

Multiobjective Fitness Functions for Stable Marriage Problem using Genetic Algrithm

Two-Sided Matching with Indifferences: Using Heuristics to Improve Properties of Stable Matchings

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