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**GARP Genetic Algorithm for Rule-set Production**

Computational Point of View

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**Presentation Outline Overview of Predictive Modeling (Arthur)**

GARP and DesktopGARP (Ricardo) Applications Climate Changes (Marinez) Risk Assessment (Raul) Agriculture (Victor) Disease Systems (Town)

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**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

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**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

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**GARP - Data and Results Point occurrence data Environmental Dimensions**

Predicted Distribution Environmental Dimensions (Environmental Layers) vegetação temperatura precipitação relevo

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**One Step at a Time GARP = GA + RP**

GA: Brief Introduction to Genetic Algorithms RP: Rule-set Production

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**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)

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**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

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**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)

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**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)

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**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%

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**Heuristic Operators Mutation: random modification of a gene Before: r2**

15 50 0k 20k A 12% After: r4 15 28 0k 20k A 15%

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**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%

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**Survival of The Fittest**

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

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**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

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**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

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**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

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**Species Modeling: DesktopGARP**

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**Acknowledgements FAPESP and NSF BRC & NHM - The University of Kansas**

DesktopGARP Testers & Users Other Collaborators

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**DesktopGARP information on-line**

Website at: Or

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**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

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DesktopGARP Thank you so much!! Any questions?

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