In AI, are there any simple and/or very visual examples of how one could implement a genome into a simulation?
Basically, I'm after a simple walkthrough (not a tutorial, but rather something of a summarizing nature) which details how to implement a genome which changes the characteristics in an 'individual' in a sumlation.
These genes would not be things like:
- Mass
- Strength
- Length,
- Etc..
But rather they should be the things defining the above things, abstracting the genome from the actual characteristics of the inhabitants of the simulation.
Am I clear enough on what I want?
Anyway, if there's any way that you have tried that's better, and that implements evolution in a form like these sexual swimmers, then by all means, go ahead and post it! The more fun inspiration the better :)
If you are implementing your 'individuals' yourself then any object can act as your genome.
Characteristics
One way to simplify this further is to turn your characteristics into enums. This way you can have a simple recombination of the parent's genes by selecting characteristics from them and a mutation of the gene by random selection of one of the enum values for a characteristic.
Once this is working you can get more nuanced with value ranges but using enums helps me keeps things clear at first.
Fitness
Then to give these characteristics meaning you need a fitness function that describes performance. The relationship between the characteristics is up to you so you can describe it in any way that makes sense. This just provides a consistant way to compare two genomes.
Simulation
Then to run a simulation just start with a few parents and generate a bunch of children to complete with each other. This can of course be automated but here is an explicit example for clarity.
Java Example
import java.util.PriorityQueue;
class Genome implements Comparable<Genome> {
public enum Mass {
LIGHT(1),
AVERAGE(2),
HEAVY(3);
final Integer value;
Mass(Integer value) {
this.value = value;
}
}
public enum Strength {
WEAK(1),
AVERAGE(2),
STRONG(3);
final Integer value;
Strength(Integer value) {
this.value = value;
}
}
public enum Length {
SHORT(1),
AVERAGE(2),
LONG(3);
final Integer value;
Length(Integer value) {
this.value = value;
}
}
private final Mass mass;
private final Strength strength;
private final Length length;
public Genome(Mass mass, Strength strength, Length length) {
this.mass = mass;
this.strength = strength;
this.length = length;
}
private Integer fitness() {
return strength.value * length.value - mass.value * mass.value;
}
@Override public int compareTo(Genome that) {
// notice the fitter is less in precedence
if(this.fitness() > that.fitness())
return -1;
else if(this.fitness() < that.fitness())
return 1;
else // this.fitness() == that.fitness()
return 0;
}
public static Genome recombine(Genome... parents) {
if(parents.length < 1)
return null;
// Select parents randomly and then characteristics from them
Mass mass = parents[(int)(Math.random() * parents.length)].mass;
Strength strength = parents[(int)(Math.random() * parents.length)].strength;
Length length = parents[(int)(Math.random() * parents.length)].length;;
return new Genome(mass, strength, length);
}
public static Genome mutate(Genome parent) {
// Select characteristics randomly
Mass mass = Mass.values()[(int)(Math.random() * Mass.values().length)];
Strength strength = Strength.values()[(int)(Math.random() * Strength.values().length)];
Length length = Length.values()[(int)(Math.random() * Length.values().length)];
return new Genome(mass, strength, length);
}
public static void main() {
PriorityQueue<Genome> population = new PriorityQueue<Genome>();
Genome parent1 = new Genome(Mass.LIGHT, Strength.STRONG, Length.SHORT);
Genome parent2 = new Genome(Mass.AVERAGE, Strength.AVERAGE, Length.AVERAGE);
Genome parent3 = new Genome(Mass.HEAVY, Strength.WEAK, Length.LONG);
population.add(parent1);
population.add(parent2);
population.add(parent3);
Genome child1 = Genome.recombine(parent1, parent2);
Genome child2 = Genome.recombine(parent1, parent2);
Genome child3 = Genome.recombine(parent1, parent3);
Genome child4 = Genome.recombine(parent1, parent3);
Genome child5 = Genome.recombine(parent2, parent3);
Genome child6 = Genome.recombine(parent2, parent3);
Genome child7 = Genome.recombine(parent1, parent2, parent3);
Genome child8 = Genome.recombine(parent1, parent2, parent3);
Genome child9 = Genome.recombine(parent1, parent2, parent3);
child1 = Genome.mutate(child1);
child2 = Genome.mutate(child2);
child4 = Genome.mutate(child4);
child8 = Genome.mutate(child8);
population.add(child1);
population.add(child2);
population.add(child3);
population.add(child4);
population.add(child5);
population.add(child6);
population.add(child7);
population.add(child8);
population.add(child9);
// and the winner is...
Genome fittest = population.peek();
}
}
Encoding
Since it sounds like you want to encode the characteristics into a sequence having some characteristics explicit in the sequence and others derived from those.
You can do this my encoding your rangle of values, like the enums above, into an integer with chunks representing your explicit characteristics.
For example if you have two explicit characteristics with four possible values each you may encode the set as an integer in the form of 00XX + XX00. So for example 0111 might corresponds to a mass of 01 and a length of 11. What this does is let you mutate by changing the bits within the sequence itself.
Java Example
import java.util.PriorityQueue;
class Genome implements Comparable<Genome> {
private final Integer sequence;
private static final Integer bitmaskChunk = 3; // ...0011
private static final Integer shiftMass = 0; // ...00XX
private static final Integer shiftLength = 2; // ...XX00
private static final Integer shiftModulus = 4; // ...0000
private Integer getMass() {
return (sequence >>> shiftMass) & bitmaskChunk;
}
private Integer getLength() {
return (sequence >>> shiftLength) & bitmaskChunk;
}
public Integer getStrength() {
return getMass() * getLength();
}
public Genome(Integer sequence) {
this.sequence = sequence % (1 << Genome.shiftModulus);
}
private Integer fitness() {
// Some performance measure
return getStrength() * getLength() - getMass() * getMass();
}
@Override public int compareTo(Genome that) {
// notice the fitter is less in precedence
if(this.fitness() > that.fitness())
return -1;
else if(this.fitness() < that.fitness())
return 1;
else // this.fitness() == that.fitness()
return 0;
}
public static Genome recombine(Genome... parents) {
if(parents.length < 1)
return null;
Integer sequence = 0;
// Select parents randomly and then characteristics from them
sequence += parents[(int)(Math.random() * parents.length)].getMass() << Genome.shiftMass;
sequence += parents[(int)(Math.random() * parents.length)].getLength() << Genome.shiftLength;
return new Genome(sequence);
}
public static Genome mutate(Genome parent) {
Integer sequence = parent.sequence;
// Randomly change sequence in some way
sequence *= (int)(Math.random() * (1 << Genome.shiftModulus));
return new Genome(sequence);
}
public static void main() {
PriorityQueue<Genome> population = new PriorityQueue<Genome>();
Genome parent1 = new Genome((int)(Math.random() * (1 << Genome.shiftModulus)));
Genome parent2 = new Genome((int)(Math.random() * (1 << Genome.shiftModulus)));
Genome parent3 = new Genome((int)(Math.random() * (1 << Genome.shiftModulus)));
population.add(parent1);
population.add(parent2);
population.add(parent3);
Genome child1 = Genome.recombine(parent1, parent2);
Genome child2 = Genome.recombine(parent1, parent2);
Genome child3 = Genome.recombine(parent1, parent3);
Genome child4 = Genome.recombine(parent1, parent3);
Genome child5 = Genome.recombine(parent2, parent3);
Genome child6 = Genome.recombine(parent2, parent3);
Genome child7 = Genome.recombine(parent1, parent2, parent3);
Genome child8 = Genome.recombine(parent1, parent2, parent3);
Genome child9 = Genome.recombine(parent1, parent2, parent3);
child1 = Genome.mutate(child1);
child2 = Genome.mutate(child2);
child4 = Genome.mutate(child4);
child8 = Genome.mutate(child8);
population.add(child1);
population.add(child2);
population.add(child3);
population.add(child4);
population.add(child5);
population.add(child6);
population.add(child7);
population.add(child8);
population.add(child9);
// and the winner is...
Genome fittest = population.peek();
}
}
I hope this is what you are looking for. Good luck.
