Biologically Inspired Computing: Introduction to Evolutionary Algorithms

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Biologically Inspired Computing Introduction
to Evolutionary Algorithms

• This is lecture two of
• Biologically Inspired Computing
• Contents
• EA intro

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Introduction to Evolutionary Computation

• Natural Evolution
• Evolutionary Algorithms
• Applications of EAs

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Natural Evolution as a Problem Solving Method

• The theory is given
• a population of organisms that can reproduce
  (generate new organisms) in a challenging/changing
  environment
• ( their chances of reproduction depends on
  how well they cope
• with their environment)
• a way of continually generating diversity in new
  child organisms (so new organisms are not
  simply copies of old ones)
• A survival of the fittest principle will
  naturally emerge future generations will have
  mixes of characteristics that tend to go along
  with being good at surviving in this environment.

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• Giraffes
• Ears

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Evolution/Survival of the Fittest

• The theory of evolution is the statement that all
  species on Earth have arisen in this way by
  evolution from one or more very simple
  self-reproducing molecules in the primeval soup.
  I.e. we have evolved via the accumulation of
  countless advantageous (in context) mutations
  over countless generations, and species have
  diversified to occupy environmental niches, as a
  result of different environments favouring
  different mutations.

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Evolvinghumans
radiotherapy treatment plans
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Evolvinghumans
lecture timetables






























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Evolution as an Optimization Method
Can view evolution as a way of solving the
problem How can I best survive in this
environment? The basic method of it is trial and
error. I.e. evolution is in the family of methods
that do something like this 1. Come up with
a new solution by randomly changing an old one.
Does it work better than previous
solutions? If yes, keep it and throw away
one of the old ones. Otherwise, discard it. 2.
Go to 1.

But this appears to be a recipe for problem
solving algorithms which take forever, with
little or no eventual success!
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The Magic Ingredients
Not so since there are certain things (and one
other sometimes useful thing) we learn from
natural evolution, which, with a sprinkling of
our own commonsense added, lead to generally
superb problem solving methods called
evolutionary algorithms Lesson0 Natural
evolution is driven by a complex environment
essentially this calculates an
organisms fitness over its lifetime. We can
replace that with a much faster
calculation! Lesson1 Keep a
population/collection of different things on the
go. Lesson2 Select parents with a
relatively weak bias towards the fittest.
Its not really plain survival of the
fittest, what works is the fitter
you are, the more chance you have to reproduce,
and it works best if even the least
fit still have some chance. Lesson3 Use
randomised Mutation and/or Recombination (aka
crossover) to generate new
candidate solutions from the selected parents

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A Generic Evolutionary Algorithm

• Suppose you have to find a solution to some
  problem or other, and suppose, given any
  candidate solution s you have a fitness function
  f(s) which measures how good s is as a solution
  to your problem.
• Generate an initial population P of randomly
  generated solutions (this is typically 100 or 500
  or so). Use f(s) to evaluate the fitness of
  each. Then
• Repeat until a termination condition is reached
• Selection Choose some of P to be parents
• Variation Apply genetic operators to the
  parents to produce some children, and then
  evaluate the fitness of the children.
• Population update Update the population P by
  retaining some of the children and removing some
  of the incumbents.

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Simple demo of power of selectionmutation
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Basic Varieties of Evolutionary Algorithm

• Selection Choose some of P to be parents
• Variation Apply genetic operators
• Population update Update the population P by

There are many different ways to select e.g.
choose top 10 of the population choose with
probability proportionate to fitness choose
randomly from top 20, etc
There are many different ways to do this, and it
depends much on the encoding (see next slide).
We will learn certain standard ways.
There are many several ways to do this, e.g.
replace entire population with the new children
choose best P from P and the new ones, etc.
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Some of what EA-ists (theorists and
practitioners) are concerned with
How to select? Always select the best? Bad
results, quickly Select almost randomly? Great
results, too slowly How to encode? The
Encoding or Representation, is the approach
used to specify specify a solution as a
datastructure. This is intricately tied up
with How to vary? (mutation,
recombination, etc) small-step mutation
preferred, recombination seems to be a
principled way to do large steps, but large
steps are usually abysmal. What parameters? How
to adapt with time?
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What are they good for ?

• Suppose we want the best possible schedule for
  a
• university lecture timetable.
• Or the best possible pipe network design for a
  ships engine room
• Or the best possible design for an antenna with
  given requirements
• Or a formula that fits a curve better than any
  others
• Or the best design for a comms network in terms
  of reliability for
• Or the best strategy for flying a fighter
  aircraft
• Or the best factory production schedule we can
  get,
• Or the most accurate neural network for a control
  problem,
• Or the best treatment plan (beam shapes and
  angles)
• for radiotherapy cancer treatment
• And so on and so on .!
• The applications cover all of optimisation and
  machine learning.

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Every Evolutionary Algorithm

• Given a problem to solve, a way to generate
  candidate solutions, and a way to assign fitness
  values
• Generate and evaluate a population of candidate
• solutions
• Select a few of them
• Breed the selected ones to obtain some new
• candidate solutions, and evaluate them
• Throw out some of the population to make way
• for some of the new children.
• Go back to step 2 until finished.

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Initial population
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Select
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Crossover
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Another Crossover
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A mutation
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Another Mutation
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Old population children
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New Population Generation 2
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Generation 3
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Generation 4, etc
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Bentley.s thesis work
Fixed wheel positions, constrained bounding area,
Chromosome is a series of slices \fitnesses
evaluated via a simple airflow simulation
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http//www.macs.hw.ac.uk/dwcorne/Teaching/iea.htm
l
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Buy it
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One of the very first applications. Determine
the internal shape of a two-phase jet nozzle that
can achieve the maximum possible thrust under
given starting conditions
Ingo Rechenberg was the very first, with
pipe-bend design. This is slightly later work in
the same lab, by Schwefel
Starting point
EA (ES) running
Result
A recurring theme design freedom ? entirely new
and better designs based on principles we dont
yet understand.
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• CW1

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CW1
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• There follow three example slides (of the kind I
  expect you to submit), and then a description of
  the coursework.

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Scheduling Earth Observing Satellites with
Evolutionary Algorithms http//alglobus.net/NASAw
ork/papers/SMCIT03/SMCIT02paper3.pdf
Example EC Application
An EOS fleet has specific observation image
capture targets and is subject to many
constraints. This looks at two cases involving
1 and 2 satellites in fixed orbits
Encoding is a Permutation of ImageTasks each
is a specific area that must be observed once per
day. A scheduler routine then determines
satellite slews and other resources that have
to be spent to achieve the requests in this order.
Fitness in these simple cases, fitness was a
combination of penalties for (i) unmet
ImageTasks, (ii) total time slweing (ii) sum of
slew angles. Hence this measured meeting of
target with minimal wear and tear and optimised
image quality.
Results HC, SA and EA were compared on these
simple cases SA was found best. Also, they found
combined scheduling was better than independent
scheduling of each satellite in a fleet
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Design of Reinforced Concrete Frames using a
Genetic Algorithmhttp//http//www.ce.memphis.edu
/pezeshk/PDFs/camp_pezeshk_hakan.pdf
Example EC Application
Design dimensions and steel reinforcement params
for structural beams meeting building constraints
Various test case scenarios looked at, including
the six storey example on the right, inolving a
set of RC elements
Encoding simple list of numbers representing
depth and height parameters, and number of
placement of steel reinforcement
sections. Fitness calculated with standard
equations used by standards bodies
Results They found that a simple GA worked
adequately, leading to small reduction in
structural costs while remaining safe and legal.
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A genetic algorithm for 2D orthogonal packing
http//www.research.att.com/techdocs/TD_7M7QJG.pdf
Example EC Application
Specific shapes (e.g. PVC, glass, plywood, ...)
have to be cut from sheet with minimal waste.
E.g. wasteful optimal solutions shown on
right.
Tested on many benchmark probs with size ranging
from 10100 shapes. Paper focuses on new fitness
function which considers the empty rectangular
spaces, aiming to help direct search towards sols
that can be more likely improved by mutation.
Encoding two permutations in each solution (i)
order of shapes (ii) order of plaement procedures
each of these is a choice from a small no. of
simple heuristics.E.g. BL means close as poss
to bottom left.
Results New technique does very well, compared
with a wide range of approaches on the same
roblems
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CW 1 BSc 3rd/4th yr Meng Students

• Produce THREE slides, each briefly describing a
  different application of evolutionary computation
  (or another bio-inspired approach) on an
  optimization problem. The previous three slides
  are examples of the type of thing I am looking
  for.
• EACH SLIDE MUST (i) contain a URL to a paper,
  thesis or other source that describes this
  application (ii) contain at least one
  graphic/figure (iii) simply and briefly explain
  key details of the problem, the encoding, the
  fitness function, and the findings in the paper.
• HOW MUCH I EXPECT FROM YOU Use google scholar,
  or maybe just google, and use sensible and
  creative search keywords. Dont go overboard in
  the time you spend on this e.g. I did not read
  in detail the papers summarised in the previous 3
  slides. I just tried to grab the key ideas, and
  make up a slide that simply conveys the gist of
  them.
• HAND IN slide 1 by 2359pm Sunday October 4th
• I will give you marks and feedback by midnight
  October 18th
• HAND IN both slide 2 and slide 3 by 2359pm
  Sunday October 25th

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CW 1 MSc and 5th yr Meng Students

• Produce TWO SETS of slides, each set containing
  TWO slides.
• Each set of two slides will briefly describe the
  application of evolutionary computation (or other
  bio-inspired approaches) on a specific
  optimization problem of your choice. Each slide
  set will compare and contrast at least three
  different papers that solve the problem in
  different ways. The previous three slides are
  therefore NOT quite examples of the type of thing
  I am looking for.
• EACH SLIDE SET MUST CONTAIN
• On slide 1 (i) URLs to the three (or more)
  sources (paper, thesis or other sources) that
  describes an application to this problem (ii) a
  clear / succinct description/explanation of the
  problem (iii) at least one graphic/figure that
  helps explain the optimization problem
• On slide 2 (i) bullet points that describe,
  compare and contrast the encodings and operators
  used in the three papers. (ii) bullet points
  that compare and contrast the results and
  findings
• of the three papers.
• HAND IN slide set 1 by 2359pm Sunday October
  4th
• I will give you marks and feedback by midnight
  October 18th
• HAND IN slide set 2 by 2359pm Sunday October
  25th

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CW1 marking and handin

• BSc students Each slide will get 0, 1, 2 or 3
  marks. There will be an additional 0 or 1 mark
  added for the diversity among your three
  applications.
• MSc and Meng 5th yr students The first slide
  (or slide set) will get 0, 1, 2, 3 or 4 marks.
  The second slide (or slide set) will get 0, 1, 2,
  3, 4, 5 or 6 marks.
• When marking the second slide (or slide set) I
  will also take into account the difference
  between the two applications
• Marking will consider how well your slide text
  and graphics conveys the things I am asking for,
  considering clarity, succinctness and
  correctness. When marking the second slide (or
  slide set) I will also take into account the
  difference between the two applications e.g.
  you will lose up to two marks if both slidesets
  are about the same optimization problem.
• To hand in, please email each individual slide in
  a separate message, as follows
• send it to dwcorne_at_gmail.com
• include the slide (either ppt or pdf) as an
  attachment
• put your (real) name and degree programme (e.g.
  BSc CS, MSc AI, whatevs) in the body of the
  email
• Make the subject line BIC CW1 Slides N, where
  N is either 1, 2, or 2 and 3

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Some extra slides if time, illustrating some
high-profile EAs
An innovative EC-designed Propellor from Evolgics
GmbH, Associated with Rechenbergs group.
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Evolving Top Gun strategies
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Evolving Top Gun strategies
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Credit Jason Lohn
NASA ST5 Mission had challenging requirements for
antenna of 3 small spacecraft. EA designs
outperformed human expert ones and are nearly
spacebound.
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Credit Jason Lohn
Oh no, we knew something like this would happen