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Department of Electronic Engineering NUIG

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Department of Electronic Engineering NUIG Direct Evolution of Patterns using Genetic Algorithms By: John Brennan Supervisor: John Maher Overview Project ... – PowerPoint PPT presentation

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Title: Department of Electronic Engineering NUIG


1
Department of Electronic Engineering NUIG
  • Direct Evolution of Patterns using Genetic
    Algorithms

By John Brennan Supervisor John Maher
2
Overview
  • Project Specifications
  • Introduction to Genetic Algorithms
  • Flowchart of a Genetic Algorithm
  • Implicit Embryogeny
  • Program Design
  • GUI Design
  • GA Parameters

3
Project Specifications
  • Review of Implicit Embryogeny proposed by Kumar
    plus 2 other authors
  • Write a Java GA to solve 1s max problem
  • Demonstrate some Java 2D GUI features
  • Extend the developed GUI to include GA
    functionality
  • Design and verify demonstrator of Implicit
    Embryogeny
  • Include Implicit Embryogeny within the developed
    GUI
  • Add additional frame to user to define target
    phenotype
  • Compare and contrast directly evolved patterns
    with implicitly derived patterns
  • Further modify the GUI to integrate in the DEV1
    algorithm

4
Introduction to Genetic Algorithms
  • A Genetic Algorithm is a programming technique
    that imitates biological evolution to solve
    complex problems
  • A GA performs the following
  • It takes a set of potential solutions (a
    population)
  • Using a selection mechanism new offspring are
    created from nominated parents
  • Genetic Operators are carried out on the
    offspring
  • The fitness of the modified offspring is
    evaluated
  • New offspring replaces previous population (a
    generation)

5
Flowchart of a Genetic Algorithm
Genome String Fitness
A 1011 127
B 1111 255
C 1010 87
Evaluated Offspring
Generate Parents
Population
F() Fitness Function
Selection Mechanism - Roulette Wheel
- Tournament
Altered Offspring
Generate Offspring
Genetic Operators
6
Implicit Embryogeny
  • An Embryogeny is a process of growth where
    genotypes (evolved parameter values) are mapped
    onto phenotypes (solutions to problems)
  • Implicit embrogenies uses interacting rules to
    solve complex problems
  • The flow of activation is dynamic, parallel and
    adaptive
  • This project will use a developmental encoding
    coding to evolve tessellating tiles similar to
    work completed by Kumar and Bentely
  • The scalability problem will also be demonstrated
    as the problem size increases (by increasing the
    size of the phenotype grid)

7
Program Design
  • GUI to be visually impressive and user friendly
  • Execute Genetic Algorithms and display results in
    real time
  • Include frames to perform the following
  • Allow user to graphically view GA progress in
    real time
  • User can input GA parameters
  • Allow user to define target phenotype
  • Integrate DEV1 Algorithm
  • To be programmed/designed in Java using J2SE and
    Java Swing

8
GUI Design
  • The main display of this program illustrates the
    ideal evolved tessellated tiles vs. the current
    output of the processing Genetic Algorithm
  • The progress of the evolving pattern will be
    displayed in real time as the GA is executed

9
GA Parameters
  • As explained, the user will be able to input
    specific parameters for the Genetic Algorithm
  • The GA will be executed according to these
    constraints
  • Further development will integrate DEV1 Algorithm
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