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Department of Electronic Engineering NUIG

Department of Electronic Engineering NUIG. Evolving Shapes using Direct & Indirect Encodings. By: John Brennan Supervisor: John Maher. Aim of Project. To compare and contrast the use of Direct and Indirect Encoding methods to evolve shapes

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Department of Electronic Engineering NUIG

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  1. Department of Electronic Engineering NUIG Evolving Shapes using Direct & Indirect Encodings By: John Brennan Supervisor: John Maher

  2. Aim of Project • To compare and contrast the use of Direct and Indirect Encoding methods to evolve shapes • To develop a visually impressive GUI to demonstrate the evolving shapes in real time • To facilitate user input of evolution parameters in the developed GUI

  3. Project Specifications • Review of Implicit Embryogeny proposed by Kumar plus 2 other authors • Write a Java GA to solve 1’s 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 shapes with implicitly derived shapes

  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

  6. GA Structure using Direct Encoding

  7. The Scalability Problem • As the size of the problem increases it reaches a point where the GA will become too inefficient to run (shown by the table below) • It is therefore necessary to implement an alternative encoding method to combat the problem of scalability

  8. Implicit Embryogeny • An Embryogeny is a process of growth where genotypes are mapped onto phenotypes • Implicit Embryogeny is a process which uses interacting rules to solve complex problems • Each genotype consists of a number of rules, and each rule has a precondition and an action • If the preconditions are met, i.e. they exceed a preset threshold, the action is carried out. • The growth is carried out in a phenotype grid

  9. Implicit Embryogeny Flowchart

  10. GA Structure using Indirect Encoding

  11. Program Design • A Java based experimental platform has been developed as part of this project • Used to visually demonstrate how the shape is evolving in Real Time • Genetic Algorithm cycles through the generations in a background process • Used to carry out experiments to demonstrate the differences between direct and indirect encoding methods

  12. GUI Design

  13. GUI Main Panel • The main panel of this GUI displays two grids • the one on the right displays the target phenotype shape • the one on the left displays the current mapping of the genotype to phenotype • The target phenotype can be selected from a number of predefined shapes which are hard coded into the application

  14. GUI Main Panel

  15. GUI Parameters Panel • The user can select if the phenotype grid should be developed using a direct or an indirect encoding method • Only text fields relevant to the current encoding technique are editable • It is dependant of the selection of a radio button which determines which text fields are masked and which can be edited • Changes in these values can have a significant effect on the results of the GA

  16. GUI Parameters Panel

  17. Results from Experiments • The experiments were performed ten times in order to obtain the average performance • GA performs well without the use of an embryogeny for smaller grid sizes but it does not scale well • Results show the improved performance with the implementation of using an embryogeny

  18. Conclusions • Use of indirect encoding methods to evolve shapes proved to show a much higher level of performance • It has also proven that the scalability problem does not affect the evolution of a phenotype which used Implicit Embryogeny to develop the desired shape. • This encoding method does have its disadvantages • It does not retain the same diversity possessed by a phenotype evolved without using an embryogeny

  19. Questions?

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