Parallel Genetic Algorithms Applied to the Traveling Salesman Problem

Jog, Prasanna; Suh, Jung-Yul; Gucht, Dirk Van

doi:10.1137/0801031

Cited by 58 publications

(24 citation statements)

References 12 publications

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“…Perhaps the most delightful application of GAs is the travelling salesperson problem, where the task is to find the shortest route for visiting a specified group of cities. Among others, [18][19][20] are just a few of the studies where GAs were implemented in travelling salesperson problem. Ref.…”

Section: Gas: a Brief Overviewmentioning

confidence: 99%

An evolutionary computing approach for parameter estimation investigation of a model for cholera

Akman

Schaefer

2015

Journal of Biological Dynamics

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We consider the problem of using time-series data to inform a corresponding deterministic model and introduce the concept of genetic algorithms (GA) as a tool for parameter estimation, providing instructions for an implementation of the method that does not require access to special toolboxes or software. We give as an example a model for cholera, a disease for which there is much mechanistic uncertainty in the literature. We use GA to find parameter sets using available time-series data from the introduction of cholera in Haiti and we discuss the value of comparing multiple parameter sets with similar performances in describing the data.

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Section: Gas: a Brief Overviewmentioning

confidence: 99%

An evolutionary computing approach for parameter estimation investigation of a model for cholera

Akman

Schaefer

2015

Journal of Biological Dynamics

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“…The encoding chosen for this genetic algorithm is the path representation used for Traveling Salesman Problems (TSP) [16,17,18]. As in the TSP, a single chromosome travels through each tract, and as the tracts are traversed, districts are formed by the sequence of tracts.…”

Section: Encodingmentioning

confidence: 99%

Congressional Districting Using a TSP-Based Genetic Algorithm

Forman

2003

Genetic and Evolutionary Computation — GECCO 2003

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Abstract. The drawing of congressional districts by legislative bodies in the United States creates a great deal of controversy each decade as political parties and special interest groups attempt to divide states into districts beneficial to their candidates. The genetic algorithm presented in this paper attempts to find a set of compact and contiguous congressional districts of approximately equal population. This genetic algorithm utilizes a technique based on an encoding and genetic operators used to solve Traveling Salesman Problems (TSP). This encoding forces near equality of district population and uses the fitness function to promote district contiguity and compactness. A post-processing step further refines district population equality. Results are provided for three states (North Carolina, South Carolina, and Iowa) using 2000 census data.

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“…Parallel genetic algorithms can be classified according to the granularity of the distributed population, coarse grained vs. fine grained, and the manner in which the GA operators are applied [39]. In a coarse-grained PGA the population is divided into several subpopulations, each of which runs a traditional GA independently and in parallel on its own subpopulation.…”

Section: Parallel Genetic Algorithmsmentioning

confidence: 99%

A Parallel Genetic Algorithm for the Set Partitioning Problem

Levine

1996

Meta-Heuristics

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Parallel Genetic Algorithms Applied to the Traveling Salesman Problem

Cited by 58 publications

References 12 publications

An evolutionary computing approach for parameter estimation investigation of a model for cholera

An evolutionary computing approach for parameter estimation investigation of a model for cholera

Congressional Districting Using a TSP-Based Genetic Algorithm

A Parallel Genetic Algorithm for the Set Partitioning Problem

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