Seminar on Computational Intelligence
Timetable
12.1.
13.1.
19.1.
20.1.
26.1.
27.1.
2.2.
3.2.
…
UBI-Health etc.
Iiro Jantunen
Introduction
Pekka Toivanen
Positioning
Mikko Asikainen
Self-Organization
Pekka Toivanen
Genetic algorithms
Pekka Toivanen
No seminar  delayed to future
Seminar presentations
Seminar presentations
Seminar on Computational Intelligence
Presentation topics
Health
1.
2.
3.
4.
5.
6.
Automatic context (behavior) analysis of elderly people
Technology for self-made measurements of health parameters
Ubiquitous health, e-health and e-Medical centre
Ubiquitous health architecture
Brain imaging and analysis in multiple sclerosis
Brain imaging and analysis in dementia
Swarm Intelligence
1.
2.
Swarm Intelligence in computer games
Basic ideas of swarm intelligence and applications
Positioning
1.
2.
3.
Positioning of animals indoors
Positioning of animals outdoors
Positioning of humans indoors
Genetic Algorithms:
A Tutorial
“Genetic Algorithms are
good at taking large,
potentially huge search
spaces and navigating
them, looking for optimal
combinations of things,
solutions you might not
otherwise find in a
lifetime.”
- Salvatore Mangano
Computer Design, May 1995
Classes of Search Techniques
Search techniques
Calculus-based techniques
Direct methods
Finonacci
Guided random search techniques
Indirect methods
Newton
Evolutionary algorithms
Evolutionary strategies
Genetic algorithms
Parallel
Centralized
Simulated annealing
Distributed
Sequential
Steady-state
Generational
Enumerative techniques
Dynamic programming
Genetic Algorithms - History
•
•
•
•
Pioneered by John Holland in the 1970’s
Got popular in the late 1980’s
Based on ideas from Darwinian Evolution
Can be used to solve a variety of problems
that are not easy to solve using other
techniques
Evolution in the real world
• Each cell of a living thing contains chromosomes - strings of
DNA
• Each chromosome contains a set of genes - blocks of DNA
• Each gene determines some aspect of the organism (like eye
colour)
• A collection of genes is sometimes called a genotype
• A collection of aspects (like eye colour) is sometimes called a
phenotype
• Reproduction involves recombination of genes from parents
and then small amounts of mutation (errors) in copying
• The fitness of an organism is how much it can reproduce
before it dies
• Evolution based on “survival of the fittest”
Start with a Dream…
•
•
•
•
Suppose you have a problem
You don’t know how to solve it
What can you do?
Can you use a computer to somehow find a
solution for you?
• This would be nice! Can it be done?
A dumb solution
A “blind generate and test” algorithm:
Repeat
Generate a random possible solution
Test the solution and see how good it is
Until solution is good enough
Can we use this dumb idea?
• Sometimes - yes:
– if there are only a few possible solutions
– and you have enough time
– then such a method could be used
• For most problems - no:
– many possible solutions
– with no time to try them all
– so this method can not be used
A “less-dumb” idea (GA)
Generate a set of random solutions
Repeat
Test each solution in the set (rank them)
Remove some bad solutions from set
Duplicate some good solutions
make small changes to some of them
Until best solution is good enough
How do you encode a solution?
• Obviously this depends on the problem!
• GA’s often encode solutions as fixed length
“bitstrings” (e.g. 101110, 111111, 000101)
• Each bit represents some aspect of the
proposed solution to the problem
• For GA’s to work, we need to be able to
“test” any string and get a “score” indicating
how “good” that solution is
Silly Example - Drilling for Oil
• Imagine you had to drill for oil somewhere
along a single 1km desert road
• Problem: choose the best place on the road
that produces the most oil per day
• We could represent each solution as a
position on the road
• Say, a whole number between [0..1000]
Where to drill for oil?
Solution1 = 300
Solution2 = 900
Road
0
500
1000
Digging for Oil
• The set of all possible solutions [0..1000] is
called the search space or state space
• In this case it’s just one number but it could
be many numbers or symbols
• Often GA’s code numbers in binary producing
a bitstring representing a solution
• In our example we choose 10 bits which is
enough to represent 0..1000
Convert to binary string
512 256 128
64
32
16
8
4
2
1
900
1
1
1
0
0
0
0
1
0
0
300
0
1
0
0
1
0
1
1
0
0
1023
1
1
1
1
1
1
1
1
1
1
In GA’s these encoded strings are sometimes called “genotypes” or
“chromosomes” and the individual bits are sometimes called “genes”
Drilling for Oil
Solution1 = 300
(0100101100)
Solution2 = 900 (1110000100)
Road
OIL
0
1000
30
5
Location
Summary
We have seen how to:
• represent possible solutions as a number
• encoded a number into a binary string
• generate a score for each number given a function of
“how good” each solution is - this is often called a
fitness function
• Our silly oil example is really optimisation over a
function f(x) where we adapt the parameter x
Search Space
• For a simple function f(x) the search space is one
dimensional.
• But by encoding several values into the
chromosome many dimensions can be searched
e.g. two dimensions f(x,y)
• Search space an be visualised as a surface or
fitness landscape in which fitness dictates height
• Each possible genotype is a point in the space
• A GA tries to move the points to better places
(higher fitness) in the the space
Fitness landscapes
Search Space
• Obviously, the nature of the search space
dictates how a GA will perform
• A completely random space would be bad
for a GA
• Also GA’s can get stuck in local maxima if
search spaces contain lots of these
• Generally, spaces in which small
improvements get closer to the global
optimum are good
Back to the (GA) Algorithm
Generate a set of random solutions
Repeat
Test each solution in the set (rank them)
Remove some bad solutions from set
Duplicate some good solutions
make small changes to some of them
Until best solution is good enough
Adding Sex - Crossover
• Although it may work for simple search
spaces our algorithm is still very simple
• It relies on random mutation to find a good
solution
• It has been found that by introducing “sex”
into the algorithm better results are
obtained
• This is done by selecting two parents during
reproduction and combining their genes to
produce offspring
Adding Sex - Crossover
• Two high scoring “parent” bit strings
(chromosomes) are selected and with some
probability (crossover rate) combined
• Producing two new offspring (bit strings)
• Each offspring may then be changed
randomly (mutation)
Selecting Parents
• Many schemes are possible so long as better
scoring chromosomes more likely selected
• Score is often termed the fitness
• “Roulette Wheel” selection can be used:
– Add up the fitness's of all chromosomes
– Generate a random number R in that range
– Select the first chromosome in the population
that - when all previous fitness’s are added gives you at least the value R
SGA operators: Selection
• Main idea: better individuals get higher chance
– Chances proportional to fitness
– Implementation: roulette wheel technique
1/6 = 17%
A
3/6 = 50%
B
C
» Assign to each individual a part of the
roulette wheel
» Spin the wheel n times to select n
individuals
fitness(A) = 3
fitness(B) = 1
2/6 = 33%
fitness(C) = 2
Example population
No.
1
2
3
4
5
6
7
8
Chromosome
1010011010
1111100001
1011001100
1010000000
0000010000
1001011111
0101010101
1011100111
Fitness
1
2
3
1
3
5
1
2
Roulette Wheel Selection
1
1
0
2
3
2
4
3
5
1
6
3
7
5
Rnd[0..18] = 7
Rnd[0..18] = 12
Chromosome4
Chromosome6
Parent1
Parent2
8
1
2
18
Crossover - Recombination
1010000000
Parent1
Offspring1
1011011111
1001011111
Parent2
Offspring2
1010000000
Crossover single
point - random
With some high probability (crossover rate) apply
crossover to the parents. (typical values are 0.8 to
0.95)
SGA operators: 1-point crossover
•
•
•
•
Choose a random point on the two parents
Split parents at this crossover point
Create children by exchanging tails
Pc typically in range (0.6, 0.9)
n-point crossover
•
•
•
•
Choose n random crossover points
Split along those points
Glue parts, alternating between parents
Generalisation of 1 point (still some positional bias)
Uniform crossover
•
•
•
•
Assign 'heads' to one parent, 'tails' to the other
Flip a coin for each gene of the first child
Make an inverse copy of the gene for the second child
Inheritance is independent of position
Mutation
mutate
Offspring1
1011011111
Offspring1
1011001111
Offspring2
1010000000
Offspring2
1000000000
Original offspring
Mutated offspring
With some small probability (the mutation rate) flip
each bit in the offspring (typical values between 0.1
and 0.001)
The GA Cycle of Reproduction
reproduction
children
modified
children
parents
population
deleted
members
discard
modification
evaluated children
evaluation
Back to the (GA) Algorithm
Generate a population of random chromosomes
Repeat (each generation)
Calculate fitness of each chromosome
Repeat
Use roulette selection to select pairs of parents
Generate offspring with crossover and mutation
Until a new population has been produced
Until best solution is good enough
Many Variants of GA
• Different kinds of selection (not roulette)
– Tournament
– Elitism, etc.
• Different recombination
– Multi-point crossover
– 3 way crossover etc.
• Different kinds of encoding other than bitstring
– Integer values
– Ordered set of symbols
• Different kinds of mutation
Many parameters to set
• Any GA implementation needs to decide on a
number of parameters: Population size (N),
mutation rate (m), crossover rate (c)
• Often these have to be “tuned” based on
results obtained - no general theory to deduce
good values
• Typical values might be: N = 50, m = 0.05, c =
0.9
Why does crossover work?
• A lot of theory about this and some
controversy
• Holland introduced “Schema” theory
• The idea is that crossover preserves “good
bits” from different parents, combining
them to produce better solutions
• A good encoding scheme would therefore
try to preserve “good bits” during crossover
and mutation
Genetic Programming
• When the chromosome encodes an entire
program or function itself this is called genetic
programming (GP)
• In order to make this work encoding is often
done in the form of a tree representation
• Crossover entials swaping subtrees between
parents
Genetic Programming
It is possible to evolve whole programs like this but only small
ones. Large programs with complex functions present big problems
Implicit fitness functions
• Most GA’s use explicit and static fitness
function (as in our “oil” example)
• Some GA’s (such as in Artificial Life or
Evolutionary Robotics) use dynamic and
implicit fitness functions - like “how many
obstacles did I avoid”
• In these latter examples other chromosomes
(robots) effect the fitness function
Problem
• In the Travelling Salesman Problem (TSP) a salesman
has to find the shortest distance journey that visits a
set of cities
• Assume we know the distance between each city
• This is known to be a hard problem to solve because
the number of possible routes is N! where N = the
number of cities
• There is no simple algorithm that gives the best
answer quickly
Problem
• Design a chromosome encoding, a mutation
operation and a crossover function for the Travelling
Salesman Problem (TSP)
• Assume number of cities N = 10
• After all operations the produced chromosomes
should always represent valid possible journeys (visit
each city once only)
• There is no single answer to this, many different
schemes have been used previously
A Simple Example
“The Gene is by far the most sophisticated program around.”
- Bill Gates, Business Week, June 27, 1994
A Simple Example
The Traveling Salesman Problem:
Find a tour of a given set of cities so that
– each city is visited only once
– the total distance traveled is minimized
Representation
Representation is an ordered list of city
numbers known as an order-based GA.
1) London 3) Dunedin
2) Venice 4) Singapore
5) Beijing 7) Tokyo
6) Phoenix 8) Victoria
CityList1
(3 5 7 2 1 6 4 8)
CityList2
(2 5 7 6 8 1 3 4)
Crossover
Crossover combines inversion and
recombination:
*
*
Parent1
(3 5 7 2 1 6 4 8)
Parent2
(2 5 7 6 8 1 3 4)
Child
(5 8 7 2 1 6 3 4)
This operator is called the Order1 crossover.
Mutation
Mutation involves reordering of the list:
Before:
*
*
(5 8 7 2 1 6 3 4)
After:
(5 8 6 2 1 7 3 4)
TSP Example: 30 Cities
120
100
y
80
60
40
20
0
0
10
20
30
40
50
x
60
70
80
90
100
Solution i (Distance = 941)
TSP30 (Performance = 941)
120
100
y
80
60
40
20
0
0
10
20
30
40
50
x
60
70
80
90
100
Solution j(Distance = 800)
TSP30 (Performance = 800)
120
100
80
y
44
62
69
67
78
64
62
54
42
50
40
40
38
21
35
67
60
60
40
42
50
99
60
40
20
0
0
10
20
30
40
50
x
60
70
80
90
100
Solution k(Distance = 652)
TSP30 (Performance = 652)
120
100
y
80
60
40
20
0
0
10
20
30
40
50
x
60
70
80
90
100
Best Solution (Distance = 420)
TSP30 Solution (Performance = 420)
120
100
80
y
42
38
35
26
21
35
32
7
38
46
44
58
60
69
76
78
71
69
67
62
84
94
60
40
20
0
0
10
20
30
40
50
x
60
70
80
90
100
Using Genetic Algorithms [GAs] to both design composite materials and aerodynamic shapes for race cars and regular
means of transportation (including aviation) can return combinations of best materials and best engineering to provide
faster, lighter, more fuel efficient and safer vehicles for all the things we use vehicles for.
Rather than spending years in laboratories working with polymers, wind tunnels and balsa wood shapes, the processes
can be done much quicker and more efficiently by computer modeling using GA searches to return a range of options
human designers can then put together however they please.
Getting the most out of a range of materials to optimize the structural and operational design of buildings,
factories, machines, etc. is a rapidly expanding application of GAs. These are being created for such uses as
optimizing the design of heat exchangers, robot gripping arms, satellite booms, building trusses, flywheels,
turbines, and just about any other computer-assisted engineering design application. T
here is work to combine GAs optimizing particular aspects of engineering problems to work together, and some of
these can not only solve design problems, but also project them forward to analyze weaknesses and possible point
failures in the future so these can be avoided
Robotics involves human designers and engineers trying out all sorts of things in order to create useful machines that
can do work for humans. Each robot's design is dependent on the job or jobs it is intended to do, so there are many
different designs out there. GAs can be programmed to search for a range of optimal designs and components for
each specific use, or to return results for entirely new types of robots that can perform multiple tasks and have more
general application.
GA-designed robotics just might get us those nifty multi-purpose, learning robots we've been expecting any year now
since we watched the Jetsons as kids, who will cook our meals, do our laundry and even clean the bathroom for us
Evolvable hardware (EH) is a new field about the use of evolutionary algorithms (EA) to
create specialized electronics without manual engineering. It brings together
reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems.
Evolvable hardware refers to hardware that can change its architecture and behavior
dynamically and autonomously by interacting with its environment.
In its most fundamental form an evolutionary algorithm manipulates a population of
individuals where each individual describes how to construct a candidate circuit. Each
circuit is assigned a fitness, which indicates how well a candidate circuit satisfies the design
specification. The evolutionary algorithm uses stochastic operators to evolve new circuit
configurations from existing ones. Done properly, over time the evolutionary algorithm will
evolve a circuit configuration that exhibits desirable behavior.
Each candidate circuit can either be simulated or physically implemented in a
reconfigurable device. Typical reconfigurable devices are field-programmable gate
arrays (for digital designs) or field-programmable analog arrays (for analog designs). At a
lower level of abstraction are the field-programmable transistor arrays that can
implement either digital or analog designs.
In many cases conventional design methods (formulas, etc.) can be used to design a circuit. But in other cases the
design specification doesn't provide sufficient information to permit using conventional design methods. For
example, the specification may only state desired behavior of the target hardware.
The fitness of an evolved circuit is a measure of how well the circuit matches the design specification. Fitness in
evolvable hardware problems is determined via two methods::
• extrinsic evolution: all circuits are simulated to see how they perform
• intrinsic evolution : physical tests are run on actual hardware.
In extrinsic evolution only the final best solution in the final population of the evolutionary algorithm is physically
implemented, whereas with intrinsic evolution every individual in every generation of the EA's population is
physically realized and tested.
Evolvable hardware problems fall into two categories: original design and adaptive systems. Original design uses
evolutionary algorithms to design a system that meets a predefined specification. Adaptive systems reconfigure an
existing design to counteract faults or a changed operational environment.
Do you find yourself frustrated by slow LAN performance, inconsistent internet access, a FAX machine that only sends
faxes sometimes, your land line's number of 'ghost' phone calls every month? Well, GAs are being developed that will
allow for dynamic and anticipatory routing of circuits for telecommunications networks. These could take notice of your
system's instability and anticipate your re-routing needs.
Using more than one GA circuit-search at a time, soon your interpersonal communications problems may really be all in
your head rather than in your telecommunications system. Other GAs are being developed to optimize placement and
routing of cell towers for best coverage and ease of switching, so your cell phone and blackberry will be thankful for GAs
too.
New applications of a GA known as the "Traveling Salesman Problem" or TSP can be
used to plan the most efficient routes and scheduling for travel planners, traffic routers
and even shipping companies. The shortest routes for traveling. The timing to avoid
traffic tie-ups and rush hours. Most efficient use of transport for shipping, even to
including pickup loads and deliveries along the way. The program can be modeling all
this in the background while the human agents do other things, improving productivity
as well! Chances are increasing steadily that when you get that trip plan packet from
the travel agency, a GA contributed more to it than the agent did
On the security front, GAs can be used both to create encryption for sensitive data as well as to break those codes.
Encrypting data, protecting copyrights and breaking competitors' codes have been important in the computer world
ever since there have been computers, so the competition is intense. Every time someone adds more complexity to
their encryption algorithms, someone else comes up with a GA that can break the code.
It is hoped that one day soon we will have quantum computers that will be able to generate completely
indecipherable codes. Of course, by then the 'other guys' will have quantum computers too, so it's a sure bet the
spy vs. spy games will go on indefinitely
The de novo design of new chemical molecules is a burgeoning field of applied chemistry in both industry and
medicine. GAs are used to aid in the understanding of protein folding, analyzing the effects of substitutions on those
protein functions, and to predict the binding affinities of various designed proteins developed by the pharmaceutical
industry for treatment of particular diseases.
The same sort of GA optimization and analysis is used for designing industrial chemicals for particular uses, and in
both cases GAs can also be useful for predicting possible adverse consequences. This application has and will continue
to have great impact on the costs associated with development of new chemicals and drugs
The development of microarray technology for taking 'snapshots' of the genes being expressed in a cell or group of
cells has been a boon to medical research. GAs have been and are being developed to make analysis of gene
expression profiles much quicker and easier. This helps to classify what genes play a part in various diseases, and
further can help to identify genetic causes for the development of diseases. Being able to do this work quickly and
efficiently will allow researchers to focus on individual patients' unique genetic and gene expression profiles,
enabling the hoped-for "personalized medicine" we've been hearing about for several years
In the current unprecedented world economic meltdown one might legitimately wonder if some of those Wall Street
gamblers made use of GA-assisted computer modeling of finance and investment strategies to funnel the world's
accumulated wealth into what can best be described as dot-dollar black holes. But then again, maybe they were simply
all using the same prototype, which hadn't yet been de-bugged. It is possible that a newer generation of GA-assisted
financial forecasting would have avoided the black holes and returned something other than bad debts the taxpayers get
to repay. Who knows
Those who spend some of their time playing computer Sims games (creating their own
civilizations and evolving them) will often find themselves playing against sophisticated
artificial intelligence GAs instead of against other human players online.
These GAs have been programmed to incorporate the most successful strategies from
previous games - the programs 'learn' - and usually incorporate data derived from
game theory in their design. Game theory is useful in most all GA applications for
seeking solutions to whatever problems they are applied to, even if the application
really is a game
When to Use a GA
• Alternate solutions are too slow or overly complicated
• Need an exploratory tool to examine new approaches
• Problem is similar to one that has already been
successfully solved by using a GA
• Want to hybridize with an existing solution
• Benefits of the GA technology meet key problem
requirements
Some GA Application Types
Domain
Application Types
Control
gas pipeline, pole balancing, missile evasion, pursuit
Design
Scheduling
semiconductor layout, aircraft design, keyboard
configuration, communication networks
manufacturing, facility scheduling, resource allocation
Robotics
trajectory planning
Machine Learning
Signal Processing
designing neural networks, improving classification
algorithms, classifier systems
filter design
Game Playing
poker, checkers, prisoner’s dilemma
Combinatorial
Optimization
set covering, travelling salesman, routing, bin packing,
graph colouring and partitioning
Conclusions
Question:
‘If GAs are so smart, why ain’t they rich?’
Answer:
‘Genetic algorithms are rich - rich in
application across a large and growing
number of disciplines.’
- David E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning