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Solving electoral zone design problems with NSGA-II: application to redistricting in Mexico

Authors:

Antonin Ponsich,

Eric A. Rincón García,

Román A. Mora Gutiérrez,

Sergio G. de-los-Cobos Silva,

Miguel A. Gutiérrez Andrade,

Pedro Lara VelázquezAuthors Info & Claims

GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 159 - 160

https://doi.org/10.1145/3067695.3076103

Published: 15 July 2017 Publication History

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Abstract

The electoral zone design problem consists in redrawing the boundaries of legislative districts for electoral purposes, in such a way that federal or state requirements are fulfilled. In Mexico, both population equality and compactness of the designed districts are considered as two conflicting objective functions. The present work represents the first intent to apply a classical Multi-Objective Evolutionary Algorithm (the NSGA-II) to this hard combinatorial problem, whereas the Mexican Federal Electoral Institute has traditionnally used a Simulated Annealing (SA) algorithm based on a weighted aggregation function. Despite some convergence troubles, the NSGA-II obtains promising results when compared with the SA algorithm, producing better-distributed solutions over a wider-spread front.

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E. A. Rincón-García, M. A. Gutiérrez-Andrade, S. G. de-los Cobos-Silva, P. Lara Velázquez, R. A. Mora-Gutiérrez, and A. Ponsich. 2012. A Discrete Particle Swarm Optimization Algorithm for Designing Electoral Zones. In Methods for decision making in an uncertain environment. World Scientific Proceedings Series on Computer Engineering and Information Science (Eds.). Reus, Spain, 174--197.

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Tomczyk MKadziński M(2024)Evolutionary algorithms for solving single- and multiple-objective political redistricting problemsApplied Soft Computing10.1016/j.asoc.2024.111258152:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111258
Nwogugu MNwogugu M(2021)Optimal Voting and Voting-Districts; and Relationships between Constitutions and the Size of GovernmentGeopolitical Risk, Sustainability and “Cross-Border Spillovers” in Emerging Markets, Volume I10.1007/978-3-030-71415-4_5(257-312)Online publication date: 31-Aug-2021
https://doi.org/10.1007/978-3-030-71415-4_5
Guest OKanayet FLove B(2019)Gerrymandering and computational redistrictingJournal of Computational Social Science10.1007/s42001-019-00053-9Online publication date: 13-Aug-2019
https://doi.org/10.1007/s42001-019-00053-9

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GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2017

1934 pages

ISBN:9781450349390

DOI:10.1145/3067695

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 15 July 2017

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GECCO '17: Genetic and Evolutionary Computation Conference

July 15 - 19, 2017

Berlin, Germany

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Tomczyk MKadziński M(2024)Evolutionary algorithms for solving single- and multiple-objective political redistricting problemsApplied Soft Computing10.1016/j.asoc.2024.111258152:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111258
Nwogugu MNwogugu M(2021)Optimal Voting and Voting-Districts; and Relationships between Constitutions and the Size of GovernmentGeopolitical Risk, Sustainability and “Cross-Border Spillovers” in Emerging Markets, Volume I10.1007/978-3-030-71415-4_5(257-312)Online publication date: 31-Aug-2021
https://doi.org/10.1007/978-3-030-71415-4_5
Guest OKanayet FLove B(2019)Gerrymandering and computational redistrictingJournal of Computational Social Science10.1007/s42001-019-00053-9Online publication date: 13-Aug-2019
https://doi.org/10.1007/s42001-019-00053-9

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A comprehensive survey on NSGA-II for multi-objective optimization and applications

Using Dominated Solutions at Edges to the Diversity and the Uniformity of Non-dominated Solution Distributions in NSGA-II

A first mathematical runtime analysis of the non-dominated sorting genetic algorithm II (NSGA-II): (hot-off-the-press track at GECCO 2022)

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