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

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Presentation on theme: "GARP Genetic Algorithm for Rule-set Production Computational Point of View."— Presentation transcript:

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

4 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 - General Approach

5 GARP - Data and Results Point occurrence data 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 Obitko's websitehttp://cs.felk.cvut.cz/~xobitko/ga/ – 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 T min_winter in [5,10] & T avg_winter in [10,22] & Elev in [1k,2k] then taxon is present R: Rule-set in GARP

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

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(r i ): fitness function – Percentage of points that are predicted correctly by the rule (can be something else)

11 Take a Look Inside GARP Rule Coding: r1: If T min_winter in [5,10] & T avg_winter in [10,22] e Elev in [1k,2k] then present r2: If T min_winter in [0,15] & T avg_winter in [0,50] & Elev in [0,20k] then absent r3: If [T min_winter x T avg_winter x (-0.2) + Elev x 0.45] then absent RuleT min_win T avg_win Elev P/Af(r) r k2kP50% r k20kA12% r A95%

12 Heuristic Operators Mutation: random modification of a gene Before: r k20kA12% r k20kA15% After:

13 Heuristic Operators Crossing over: Exchange of segments between two chromossomes: Before: r k2kP50% r k20kA12% After : r k20kP87% r k2kA9%

14 Survival of The Fittest Rulef(r) r395% r587% r150% r415% r212% r69% Rules Sorted by f(r)

15 Survival of The Fittest Rulef(r) r395% r587% r150% r415% r212% r69% Threshold Survice and have offspring Die

16 Results Rulef(r) r395% r587% r150% Survivors form a rule set that represents the ecological niche of that species After iterations:

17 Results Ecological Niche Model of the Species: r3: If [T min_winter x T avg_winter x (-0.2) + Elev x 0.45] then absent r5: If T min_winter in [5,10] & T avg_winter in [10,50] & Elev in [0,20k] then present r1: If T min_winter in [5,10] & T avg_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?


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