# GARP Genetic Algorithm for Rule-set Production

## Presentation on theme: "GARP Genetic Algorithm for Rule-set Production"— Presentation transcript:

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

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

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

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

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

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

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)

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

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)

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)

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%

Heuristic Operators Mutation: random modification of a gene Before: r2
15 50 0k 20k A 12% After: r4 15 28 0k 20k A 15%

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%

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

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

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

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

Species Modeling: DesktopGARP

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

DesktopGARP information on-line
Website at: Or

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

DesktopGARP Thank you so much!! Any questions?

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