A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems

Wolters, Mark A.

doi:10.18637/jss.v068.c01

Cited by 29 publications

(24 citation statements)

References 13 publications

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“…) was used to identify the network configuration that optimizes yield and persistence. The optimization was based on the method kofnGA in the R package of the same name, a genetic algorithm for subset selection that minimizes a user‐defined objective function for that subset (Wolters ). Each run was carried out 300 iterations, and the whole process was repeated 300 times (details on the method and sensitivity analyses in Supporting Information).…”

Section: Methodsmentioning

confidence: 99%

A Genuine Win‐Win: Resolving the “Conserve or Catch” Conflict in Marine Reserve Network Design

Chollett

Garavelli

O’Farrell

et al. 2016

CONSERVATION LETTERS

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To support fishing communities, reserves should ensure the persistence of meta-populations while boosting fisheries yield. However, so far their design from the onset has rarely considered both objectives simultaneously. Here we overcome this barrier in designing a network of reserves for the Caribbean spiny lobster, a species with long larval duration for which local management is considered pointless because the benefits of protection are believed to be accrued elsewhere. Our reserve design approach uses spatially explicit population models and considers ontogenetic migration, larval and adult movement. We show that yield and persistence are negatively related, but that both objectives can be maximized simultaneously during planning. Importantly, we also show that local efforts to manage spiny lobster, the most economically valuable marine resource in the Caribbean, can result in locally accrued benefits, overcoming a major barrier to investing effort in the appropriate management of this species.

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Section: Methodsmentioning

confidence: 99%

A Genuine Win‐Win: Resolving the “Conserve or Catch” Conflict in Marine Reserve Network Design

Chollett

Garavelli

O’Farrell

et al. 2016

CONSERVATION LETTERS

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“…To identify the optimal subset of locations that minimize or , we used a genetic algorithm implemented by the function in the package (Sutherland et al, 2019). This function is a wrapper of the function from its namesake package, (Wolters, 2015) with additional arguments to extend the function’s utility for generating SCR sampling designs. The k-of-n problem is an appropriate application as it describes concisely the challenge of the SCR sampling design problem where some number of traps, k , must be placed in a landscape comprised of n possible locations and configured to optimize some objective function, which presented here is one of two SCR-specific criteria.…”

Section: Methodsmentioning

confidence: 99%

Optimal sampling design for spatial capture-recapture

Dupont

Royle

Nawaz

et al. 2020

Preprint

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10Spatial capture-recapture (SCR) has emerged as the industry standard for 11 analyzing observational data to estimate population size by leveraging information from 12 spatial locations of repeat encounters of individuals. The resulting precision of density 13 estimates depends fundamentally on the number and spatial configuration of traps.14 Despite this knowledge, existing sampling design recommendations are heuristic and 15 their performance remains untested for most practical applications -i.e., 16spatially-structured and logistically challenging landscapes. To address this issue, we 17 propose a genetic algorithm that minimizes any sensible, criteria-based objective 18 function to produce near-optimal sampling designs. To motivate the idea of optimality, 19 we compare the performance of designs optimized using two model-based criteria 20 related to the probability of capture. We use simulation to show that these designs 21 out-perform those based on existing recommendations in terms of bias, precision, and 22 accuracy in the estimation of population size. Our approach allows conservation 23 practitioners and researchers to generate customized sampling designs that can improve 24 monitoring of wildlife populations. 25Keywords-SCR, spatial capture-recapture, spatially-explicit capture-recapture, 26 camera traps, density, optimal design, sampling design, spatial sampling, trap spacing 27 29 (Williams et al., 2002) which has driven the development of data collection and estimation 30 methods, especially those that can account for imperfect detection. Capture-recapture (CR), 31 and more recently, spatial capture-recapture (SCR: Royle et al., 2014) methods were 32 developed specifically for this purpose and are now routinely applied in ecological research. 33 Concurrently, SCR methods estimate detection, space use, and density by analyzing 34 2 individual encounter histories while explicitly incorporating auxiliary information from the 35 spatial organization of encounters (Efford, 2004; Royle et al., 2014). Despite widespread 36 adoption and rapid method development, recommendations about spatial sampling design 37 have received relatively little attention and are arguably heuristic. 38 The effects of sampling design have been investigated for both CR (Dillon and Kelly 39 2007; Bondrup-Nielsen 1983; Gardner et al. 2010) and SCR methods (discussed in the next 40 paragraph). While CR methods aim to balance the number of captures and the number of 41 recaptures, SCR requires a third consideration, the number of spatial recaptures, i.e., the 42 number of times individuals are observed at multiple locations. The ability to reliably 43 estimate these quantities is directly related to the quality of the data collected: the number of 44 captured individuals n is the sample size; the number of recaptures is directly related to the 45 baseline detection probability, g 0 ; and the number and spatial distribution of recaptures are 46 directly related to the spatial scale parameter, σ. Therefore, improving sampling design...

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“…We have used both of these methods and found they yield models with similar predictive capability. Our computing was done in the R environment [ 19 ], where packages glmnet [ 7 ] and kofnGA [ 26 ] can be used for shrinkage estimation and genetic algorithm search, respectively.…”

Section: Methodsmentioning

confidence: 99%

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards

Wolters

Dean

2016

Stat Biosci

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Remote sensing images from Earth-orbiting satellites are a potentially rich data source for monitoring and cataloguing atmospheric health hazards that cover large geographic regions. A method is proposed for classifying such images into hazard and nonhazard regions using the autologistic regression model, which may be viewed as a spatial extension of logistic regression. The method includes a novel and simple approach to parameter estimation that makes it well suited to handling the large and high-dimensional datasets arising from satellite-borne instruments. The methodology is demonstrated on both simulated images and a real application to the identification of forest fire smoke.

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A Genetic Algorithm for Selection of Fixed-Size Subsets with Application to Design Problems

Cited by 29 publications

References 13 publications

A Genuine Win‐Win: Resolving the “Conserve or Catch” Conflict in Marine Reserve Network Design

A Genuine Win‐Win: Resolving the “Conserve or Catch” Conflict in Marine Reserve Network Design

Optimal sampling design for spatial capture-recapture

Classification of Large-Scale Remote Sensing Images for Automatic Identification of Health Hazards

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