1. GARP Genetic Algorithm for Rule-set Production
Computational
Point of View

2. Presentation Outline Overview of Predictive Modeling (Arthur)
GARP and DesktopGARP (Ricardo)
Applications
Climate Changes (Marinez)
Risk Assessment (Raul)
Agriculture (Victor)
Disease Systems (Town)

3. Predictive Modeling Methodology
Occurrence Points
Algorithm
Precipitation
Temperature
Ecological Niche Model
Slilde by Town Peterson
Invasive
Potential
Projection Onto
Another Region
Predicted Distribution
After Climate Changes
Projection
Over Changed
Climate
Native
Predicted
Range
Geography Ecology

4. GARP - General Approach
Divide data in training data set (used to build models) and test data set (to validate the model)
Applies an algorithm to the training data set
BIOCLIM
Logistic Regression
etc.
Evaluates model quality, by asking how errors are different from random

5. GARP - Data and Results Point occurrence data Environmental Dimensions
Predicted Distribution
Environmental
Dimensions
(Environmental
Layers)
vegetação
temperatura
precipitação
relevo

6. One Step at a Time GARP = GA + RP
GA: Brief Introduction to Genetic Algorithms
RP: Rule-set Production

7. GA: Genetic Algorithms
Application from Artificial Intelligence
Concept taken from genetics and evolutions of species applied as a generic problem solving technique in Computer Science:
- Genes, chromossomes, mutations, insertions, deletions, crossing over, genotype, individuals, population, survival of the fittest.
For more info on GA,
visit: Marek Obitko's website at
Or ask me during the demo sessions (during lunch time)

8. RP: Rule-set Production
Rule: Logical Proposition
Format:
If A is true then B
A: precondition
B: result or prediction
Example:
If Tmin_winter in [5,10] & Tavg_winter in [10,22] & Elev in [1k,2k]
then taxon is present
R: Rule-set in GARP

9. Training Data-set P: Set of training data point (spp)
Elements in P: pi = (a, b)
a: environmental variables at that point
b: observed presence or absence
Example: p1 = (10, 12, 2k, Present)

10. Rule-set Evaluation P is used to test the rules in R:
If a in A:
If b=B then the rule predicts correctly
If b≠B then the rule DOES NOT predicts correctly
If a not in A:
Rule does not apply to the point: test next rule
f(ri): fitness function
Percentage of points that are predicted correctly by the rule (can be something else)

11. Take a Look Inside GARP Rule Coding: P/A f(r) r1 5 10 22 1k 2k P 50%
r1: If Tmin_winter in [5,10] & Tavg_winter in [10,22] e Elev in [1k,2k] then present
r2: If Tmin_winter in [0,15] & Tavg_winter in [0,50] & Elev in [0,20k] then absent
r3: If [Tmin_winter x Tavg_winter x (-0.2) + Elev x 0.45] then absent
Rule
Tmin_win
Tavg_win
Elev
P/A
f(r) r1 5 10 22 1k 2k P
50% r2 15 50 0k
20k A
12% r3
0.8
---
-0.2
0.45
95%

12. Heuristic Operators Mutation: random modification of a gene Before: r215 50 0k
20k A
12%
After: r4 15 28 0k
20k A
15%

13. Heuristic Operators r1 5 10 22 1k 2k P 50% r2 15 50 0k 20k A 12% r5 5
Crossing over: Exchange of segments between two chromossomes:
Before: r1 5 10 22 1k 2k P
50% r2 15 50 0k
20k A
12%
After: r5 5 10 50 0k
20k P
87% r6 15 22 1k 2k A 9%

14. Survival of The Fittest
Rule
f(r) r3
95% r5
87% r1
50% r4
15% r2
12% r6 9%
Rules Sorted by f(r)

15. Survival of The Fittest
Rule
f(r) r3
95% r5
87% r1
50% r4
15% r2
12% r6 9%
Survice and have offspring
Threshold
Die

16. Results After <n> iterations: Rule f(r) r3 95% r5 87% r1 50%
Survivors form a rule set that represents the ecological niche of that species

17. Results Ecological Niche Model of the Species:
r3: If [Tmin_winter x Tavg_winter x (-0.2) + Elev x 0.45] then absent
r5: If Tmin_winter in [5,10] & Tavg_winter in [10,50] & Elev in [0,20k] then present
r1: If Tmin_winter in [5,10] & Tavg_winter in [10,22] & Elev in [1k,2k] then present
Rule-set projection back onto the geography space
Model test, overlaying test points evaluating how those points are predicted

18. Species Modeling: DesktopGARP

19. Acknowledgements FAPESP and NSF BRC & NHM - The University of Kansas
DesktopGARP Testers & Users
Other Collaborators

20. DesktopGARP information on-line
Website at: Or

21. Stay With Us For More GARPing
Next: Lifemapper Project
Demo Session during Lunch Time:
Genetic Algorithms in General
GARP Algorithm
DesktopGARP live demo
In the Afternoon: Many Neat Applications

22. DesktopGARP
Thank you so much!!
Any questions?