Information Flocking Applied to Genetic

Programming VisualizationMatthieu Macret

Outline• Introduction

• Genetic Programming (GP)

• Challenge

• The visualization problem

• Related works

• Information flocking for GP visualization

• Data

• Design choices

• Implementation

• Demonstration

• Issues

• Conclusion

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Genetic Programming

• Principle : Evolving a population of computer programs.

• Makes possible to explore huge research space and find solutions to optimization problems.

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Problem example

• SANTINI, C., ZEBULUMI, R., PACHECO, M., VELLASCO, M., AND SZWARCMAN, M. 2001. “Evolution of analog circuits on a programmable analog multiplexer array”.

• IMBRIALE, W. 1991. “Evolution of the large deep space network antennas”.

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Illustration example

Find a polynomial function to approximate this function.

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Data representation

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+

X

-

/5 x

x 33

Nodes

Terminals

12=

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Evaluation fitness

• Fitness function is a metric to measure how close a candidate solution is to the optimal solution.

• Each individual of the population is evaluated with this fitness function.

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Illustration example

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Function to approximate Candidate solution

Possible fitness function = distance between the two function

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Example selection

n=3Tournament selection

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Reproduction

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Crossover

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Crossover

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Crossover

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Crossover

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Crossover

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Crossover

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Mutation

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Mutation

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Mutation

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Termination criteria

• Reach the fitness level specified

• Reach the maximum number of generations

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Evolution parameters

• Parameters :

• number of generations,

• number of individuals per generation,

• max/min depth tree,

• cross-over rate,

• mutation rate,

• initialization method...

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Challenges in GP

• For complex problems, the fitness function is difficult to design.

• Finding the good set of GP parameters is not straightforward.

• Bloat and loss of diversity phenomenon.

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Bloat

No real explanation so far

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Diversity

Proportion of similar individuals

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GP Visualization Problem

• Large dataset :

• hundreds of individuals per generation

• hundreds of generations

• Varying data

• Genealogy between individuals

• Genetic operations (crossover, selection, mutation)

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Usual visualization

http://mmacret.perso.centrale-marseille.fr/metacreation/experiments/experiment3/index.html

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Issues

• Do not explain the dynamics of GP

• Give a global idea of the evolution but do not allow a exploration at the individual level

• Do not explain bloat or loss of diversity

• Do not give any clue to choose better GP parameters.

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Related work

• JASON M. DAIDA, ADAM M. HILSS, DAVID J. WARD AND STEPHEN L. LONG, 2004,“Visualizing Tree Structures in Genetic Programming”.

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System design

• Motivation:

• Visualize the dynamics of the GP system

• Make possible the exploration at several levels (population/individual/overall evolution)

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REYNOLDS, C. Flocks, herds and schools: A distributed behavioral model. Computer Graphics, 1987.

Behavior rules

• Each agent (fish) has internal behavior rules:

• Collision avoidance

• Velocity matching

• Flock centering

➡Emergence: natural movement of fishes !

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Information Flocking applied to Time-Varying Data Visualization

• A. Moere, Time-Varying Data Visualization using Information Flocking Boids. Infoviz 2004.

➡

Showed that the original information flocking principles extended with dynamically generated motion technologies is able to represent the dynamics of time-varying dataset.

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Extended flocking model

• In addition of the original flocking behavior rules, 2 data-dependant clustering rules:

• Data similarity

• Data dissimilarity

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• Movement:

• Conveys the feeling of dynamics,

• Has a strong and effective perceptual grouping effect. (clustering)

• Emergence:

• Make possible to identify interesting patterns and relationships.

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Why using information flocking ?

Data

PopulationsPopulationsPopulationsPopulationsPopulations

gen indId fitness size depth

OperationsOperationsOperationsOperationsOperations

gen indIdactionType

parentId1

parentId2

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ParticuleSize ~ Ind. tree size

Length ~ Ind. tree depth

Color ~ Ind. fitness

Gradient red -> green

size

depth

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Environment

• 3D environment:

• More dimensions = more space to cluster

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Genealogy

Vizualize two consecutive generations

with strokes = generation-1

without strokes = generation

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Crossover

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Mutation

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Interactivity

• Possible to select a individual on the screen

• Get more precise data (fitness, indId)

• Get the corresponding tree

• Navigation in the 3D space

• Pause the vizualization

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Behavior rules

• Collision avoidance

• Velocity matching

• Flock centering

• Data similarity

• Data dissimilarity

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Implementation

DatabasePostgreSQL

ProcessingToxiclibs library

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Demonstration

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Issues

• Get the data from my system.

• Modify the flocking algorithm to make possible the creation of new individuals by crossover or mutation.

• Technical issues

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Conclusions• What I was expected to get:

• Outlying dynamic patterns

• Valid at the individual level

• Valid at the whole dataset level

• Show long-term data value evolution

• Short-term significant events

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Discussion

• What could be a good way to evaluate this kind of systems ?

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