This repository is just an experimentation around genetic algorithms.
Most credits on this code goes to github user Murgio, whose code I refactored and optimized. See his Generic Algorithm Montage repository.
Package fr.jblezoray.mygeneticalgo contains an implementation of a generic algorithm. Then, there are sample applications of the genetic algorithm in packages fr.jblezoray.mygeneticalgosample.*.
Generates an "hello world" :
Generation 100 (0,00633)--> Ldkmo!xoqlc ! Lc!Lpqek.Lnrul ept!ugmpkdoeno.gu!gaww sewte fmnimzé!damt la cqlpntisklm.er lc ngua hl pbde fydnr fmoressipn.
Generation 150 (0,01639)--> Hdkmo worlc ! Le!Lpsem!Iorum ett!simpmdment bu!gauy sewte flomnzé!damr la colporision.er lc misf hn pbfe avanr imoressipn.
Generation 200 (0,02778)--> Heklo world ! Le!Losem!Ipsum est silpmement cu eauy tewte emokozé!danr la composition.eu la!misc en pafe avant imoressinn.
Generation 250 (0,12500)--> Hemko world ! Le!Lorem Ipsum est simplement cu eaux texte employé!daos la comppsition et la mise en page avant impression.
Generation 300 (0,25000)--> Helko world ! Le!Lorem Ipsum est simplement cu faux texte employé dans la composition es la mise en page avant impression.
Generation 350 (0,33333)--> Hello world ! Le Lorem Ipsum est simplement cu!faux texte!employé dans la composition et la mise en page avant impression.
Generation 400 (0,25000)--> Iello world ! Le Lorem Ipsum est simplement cu!faux texte employé dant la composition et la mise en page avant impression.
Generation 450 (0,25000)--> Hello world ! Le Lnrem Ipsum est simpldment cu faux texte employé dant la composition et la mise en page avant impression.
Generation 500 (0,20000)--> Hello world ! Le Lorem Ipsum est simpldmeot!cu fbux texte employé dans la composition et la mise en page avant impression.
Generation 550 (0,25000)--> Hello world ! Le Lorem Ipsum est sjmpldment cu fbux texte employé dans la composition et la mise en page avant impression.
Generation 600 (0,25000)--> Hello world ! Le Lorem Ipsum est simplfment cu fbux texte employé dans la composition eu la mise en page avant impression.
Generates a face by combinating instances of itself.
input:
generation 1, 100, 500, 1000, and 2000:
I've tried to optimize the comparison of the generated image with the original one (the fitness evaluation). Here are a list of the algorithms I tried : samples/eval_fitness.pdf (in french !).
This sample generates an Generates an approximation of an image by composing arbitrary disks.
input:
output:
generation 200, 1000, 2000, 5000, and 10000:
Install a java jdk >=8 and maven >=3.0.5.
First, compile the project :
$ mvn package
Run sample #1 (helloworld) :
$ java -cp target/myGeneticAlgo-0.0.1-SNAPSHOT.jar fr.jblezoray.mygeneticalgosample.helloworld.Main
Run sample #2 (facemashup) :
$ mkdir statusDir
$ java -cp target/myGeneticAlgo-0.0.1-SNAPSHOT.jar fr.jblezoray.mygeneticalgosample.facemashup.Main samples/facemashup/match.png samples/facemashup/mask.png statusDir
The statusDir contains intermediate results.
Run sample #2 (disksimage) :
$ mkdir statusDir
$ java -cp target/myGeneticAlgo-0.0.1-SNAPSHOT.jar fr.jblezoray.mygeneticalgosample.disksimage.Main samples/disksimage/monalisa_croped.png statusDir
Have fun !
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