# Genetic Algorithms

AI and Robotics

Oct 24, 2013 (5 years and 2 months ago)

153 views

G
ENETIC

A
LGORITHMS

Steve Foster

I
NTRODUCTION

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

H
OW

WE

EVOLVE

Natural Selection

Strong members of a population survive to
reproduce, passing on their ‘strong’ traits

Crossover

Some from parent A, some from parent B

Mutation

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

B
IOLOGY

TO

GENETIC

ALGORITHM

Gene = smallest atom of data

Usually binary, so 0 or 1

Genome = string of genes

0111010010010101

Genome Pool = set of genomes

Represents our population

T
HE

BASIC

I
DEA

genome pool

Each of these decomposes to a (potential)
solution to our problem

Gradually evolve our way to a
solution.

F
ITNESS

/ S
TRENGTH

H
EURISTIC

Turns a binary string into a single
floating point value based on how
close to our desired result it is

Maps back to how well an organism can
survive in an environment

0.0f = terrible

1.0f = absolute solution

A B
ASIC

G
ENETIC

A
LGORITHM

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.

R
EAL

W
ORLD

P
ROBLEMS

There are a number of different
issues that need to be addressed

Representing the problem

Assessing fitness

Determining selection

Modelling crossover and mutation

S
ELECTION

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

S
ELECTION

Roulette Wheel Selection

Fitness proportionate

Probabilistically select a number of members of the

S
ELECTION

Rank selection

Calculate the fitness of each hypothesis

Arrange them in decreasing order of
fitness

Pick the fittest
n

hypothesis for mating

S
ELECTION

Tournament Selection

Two hypothesis are chosen at random
from the current population

The fittest solution is selected for
survival and mating

The selection criteria yields a more
diverse gene pool than roulette wheel
selection

C
ROSSOVER

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

S
IMPLE

C
ROSSOVER

Parents

Children

M
ULTIPLE

P
OINT

C
ROSSOVER

Parents

Children

M
UTATION

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

P
ATHFINDING

E
XAMPLE

(1)

Example:

2D grid

Arbitrary number of boundaries

1 start point

1 finish point

P
ATHFINDING

E
XAMPLE

(2)

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

00 = up

01 = right

10 = down

11 = left

P
ATHFINDING

E
XAMPLE

(3)

Heuristic function:

Simulate binary string movement
beginning at start point

Measure distance from finish (simple,
Pythagorean)

Fitness score = 1

(distance / max
possible distance)

P
ATHFINDING

E
XAMPLE

(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

P
ATHFINDING

E
XAMPLE

(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

P
ATHFINDING

E
XAMPLE

(6)

Genome A: 01 10 01 10 01 00 01 00

Genome 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)

P
ATHFINDING

E
XAMPLE

(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

T
HINGS

WE

CAN

T
WEAK

Mutation rate

0.01 is a reasonable starting value

Crossover rate

0.7 or so

Chromosome length

V
aries a lot based on specific problem

Population size

Try maybe 150
-
250

U
SE

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

E
XAMPLES

Some games run it in real time

Black and White

Quake 3 bot AI

Used 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)