GENETIC ALGORITHMS

Steve Foster

INTRODUCTION

Genetic Algorithms are based on the principals of evolutionary biology in order to find solutions to problems

In nature the evolution of species is a successful and robust method for ensuring that biological systems survive in their environment

It can be seen as a search problem, in which the survival of solutions is determined by a form of natural selection

HOW WE EVOLVE

Natural SelectionStrong members of a population survive to

reproduce, passing on their ‘strong’ traits

CrossoverSome from parent A, some from parent B

MutationA strange flipped gene that cannot be traced back to a parent

BIOLOGY TO GENETIC ALGORITHM

Gene = smallest atom of dataUsually binary, so 0 or 1

Genome = string of genes0111010010010101

Genome Pool = set of genomesRepresents our population

THE BASIC IDEA

Start with a completely random genome poolEach of these decomposes to a (potential)

solution to our problem

Gradually evolve our way to a solution.

FITNESS / STRENGTH HEURISTIC

Turns a binary string into a single floating point value based on how close to our desired result it isMaps back to how well an organism can

survive in an environment

0.0f = terrible1.0f = absolute solution

A BASIC GENETIC ALGORITHM

Start with a random genome pool of n members.

Run strength heuristic on each random genome in the pool.

‘Randomly’ crossbreed strong members of the population until you have n new genomes.

Introduce some mutation. Repeat until the strength heuristic

returns a value within our threshold.

REAL WORLD PROBLEMS

There are a number of different issues that need to be addressedRepresenting the problem

Assessing fitness

Determining selection

Modelling crossover and mutation

SELECTION

Determines the survival of the fittest

How can we determine the value of genetic material? Could be a good substring within an overall poor

solution

Therefore it may be worth saving some of the weaker solutions

SELECTION

Roulette Wheel Selection Fitness proportionate

Probabilistically select a number of members of the population to add crossbreed

SELECTION

Rank selectionCalculate the fitness of each

hypothesis

Arrange them in decreasing order of fitness

Pick the fittest n hypothesis for mating

SELECTION

Tournament SelectionTwo hypothesis are chosen at

random from the current populationThe fittest solution is selected for

survival and matingThe selection criteria yields a more

diverse gene pool than roulette wheel selection

CROSSOVER

Crossover is the process of mating in order to combine the genetic material of fit solutions

There are a number of different ways to combine two hypothesis, which lead to differences in future populations

The simplest method takes the two parents and creates two children by combining the two halves of each solution

SIMPLE CROSSOVER

Parents

Children

MULTIPLE POINT CROSSOVER

Parents

Children

MUTATION

Random mutation is a feature of conventional genetics where accidents of nature lead to random changes in the genetic makeup

In some cases these changes can be disastrous, in other cases they can be highly advantageous

In binary strings Can be modelled by randomly flipping a small

percentage of bits In other representations

Randomly change one element of a child solution a very small percentage of the time

PATHFINDING EXAMPLE (1)

Example:2D grid

Arbitrary number of boundaries

1 start point

1 finish point

PATHFINDING EXAMPLE (2)

Break down binary string into movements across a 2D grid.00 = up

01 = right

10 = down

11 = left

PATHFINDING EXAMPLE (3)

Heuristic function:Simulate binary string movement

beginning at start point

Measure distance from finish (simple, Pythagorean)

Fitness score = 1 – (distance / max possible distance)

PATHFINDING EXAMPLE (4)

start

Genome A: 01 10 01 10 01 00 01 00

01 = Right

10 = Down

01 = Right (Bump)

10 = Down

01 = Right

00 = Up (Bump)

01 = Right

00 = Up

Fitness = 1 - (2 / 24)

finish

PATHFINDING EXAMPLE (5)

start

Genome B: 00 01 10 10 11 10 01 01

00 = Up

01 = Right

10 = Down

10 = Down

11 = Left

10 = Down

01 = Right

01 = Right

Fitness = 1 – (2 / 24)

finish

PATHFINDING EXAMPLE (6)

Genome A: 01 10 01 10 01 00 01 00Genome B: 00 01 10 10 11 10 01 01

Genome C: 01 10 01 10 01 00 01 01

This is how we take two genomes and create a new one:

(assumes no mutation for now)

PATHFINDING EXAMPLE (7)

start

01 = Right

10 = Down

01 = Right (Bump)

10 = Down

01 = Right

00 = Up (Bump)

01 = Right

01 = Right

Fitness = 1 – (0 / 24)

finish

Genome C: 01 10 01 10 01 00 01 01

THINGS WE CAN TWEAK

Mutation rate0.01 is a reasonable starting value

Crossover rate0.7 or so

Chromosome length Varies a lot based on specific problem

Population sizeTry maybe 150-250

USE IN GAMES

Computationally expensive, becoming easier to deal with as hardware speeds up

Most of the time is run offline with the results used in a ‘black box’ fashion in the actual game

Can be used to tune priorities, behaviors, parameters, etc

EXAMPLES

Some games run it in real timeBlack and White

Quake 3 bot AIUsed to optimize the fuzzy logic controller

AI I am:

45% in favor of grabbing that rocket launcher 62% in favor of picking up the red armor 89% in favor of the Quad Damage

Check out the source code (GPL)