Department of Electronic Engineering NUIG Direct Evolution of Patterns using Genetic Algorithms By: John Brennan Supervisor: John Maher

Overview Project Specifications Project Specifications Introduction to Genetic Algorithms Introduction to Genetic Algorithms Flowchart of a Genetic Algorithm Flowchart of a Genetic Algorithm Implicit Embryogeny Implicit Embryogeny Program Design Program Design GUI Design GUI Design GA Parameters GA Parameters

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

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

Flowchart of a Genetic Algorithm GenomeStringFitness A B C Genetic Operators F() Fitness Function Population Altered Offspring Generate Offspring Selection Mechanism - Roulette Wheel - Tournament Evaluated Offspring Generate Parents

Implicit Embryogeny An Embryogeny is a process of growth where genotypes (evolved parameter values) are mapped onto phenotypes (solutions to problems) 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 Implicit embrogenies uses interacting rules to solve complex problems The flow of activation is dynamic, parallel and adaptive 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 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) The scalability problem will also be demonstrated as the problem size increases (by increasing the size of the phenotype grid)

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

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

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