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Heuristic Algorithm Teaching Tool (HATT):

HATT was modified 9/25/03 - Simulated annealing problem

HATT was modified 10/27/03 - Needed double-precision declaration on some variables and arrays to prevent data seepage

HATT was modified 12/2/03 - Needed GA assignment of best child's fitness to GA_fitness array in GA_Global_subs.bas

HATT was modified 12/4/03 - Needed GA logic to prevent ignoring child 1 when child 2 was infeasible (had a fitness of -99)

HATT was modified 4/20/04 - Minor reporting problem with Weibull curve estimation process (number of samples not written to output file)

HATT was modified 4/17/08 - Raindrop code added

For those who only want the HATT .exe program:

Executable program

For those who only want the HATT program (not the underlying code) in a setup.exe format:

Setup.exe program

For those want the HATT program, and the underlying VB code:

Visual Basic Code

Example data files:

Southern US example forest

Western US example forest

Northern US example forest

Cite the use of the HATT model as:

Bettinger, P. 2003. Heuristic Algorithm Teaching Tool (HATT). Warnell School of Forest Resources, University of Georgia, Athens, GA.

Documentation of the model:

Bettinger, P. 2005. HATT: The heuristic algorithm teaching tool for advanced forest planning courses. In Systems Analysis in Forest Resources: Proceedings of the 2003 Symposium, Bevers, M., and T.M. Barrett (Comps.). USDA Forest Service, Pacific Northwest Research Station, Portland, OR. General Technical Report PNW-GTR-656. pp. 61-70.

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List of Databases Available:

The "Wildlife Challenge"

Lincoln Tract GIS data

Putnam Tract GIS data

Daniel Pickett Forest GIS data

Brown Tract DEM

Brown Tract shape files

Brown Tract orthophotograph

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FORS 8450 Lectures and Assignments

Lecture 1

Lecture 2

Lecture 3

Lecture 4

Lecture 5

Lecture 6

Lecture 7

Lecture 8

Lecture 9

Lecture 11

Lecture 12

Lecture 14

Lecture 19

Lecture 20

Lecture 21

Lecture 22

Lecture 23

Lecture 24

Lecture 31

Lecture 32

Assignment 1 - LP

Assignment 2 - Monte Carlo

Assignment 3 - Threshold Accepting

Assignment 4 - Simulated Annealing

Assignment 5 - Tabu Search

Assignment 6 - Genetic Algorithm

Assignment 7 - Raindrop Method

Assignment 8 - Binary Search

Assignment 9 - Mixed Integer Programming

Assignment 10 - Estimating a global optimum

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