Genetic_Algorithms_genetic for_data .ppt

A.E. Eiben and J.E. Smith, Introduction to Evolutionary Computing
Genetic Algorithms
GA Quick Overview
 Developed: USA in the 1970’s
 Early names: J. Holland, K. DeJong, D. Goldberg
 Typically applied to:
– discrete optimization
 Attributed features:
– not too fast
– good heuristic for combinatorial problems
 Special Features:
– Traditionally emphasizes combining information from good
parents (crossover)
– many variants, e.g., reproduction models, operators

Genetic Algorithms
Genetic algorithms
 Holland’s original GA is now known as the
simple genetic algorithm (SGA)
 Other GAs use different:
– Representations
– Mutations
– Crossovers
– Selection mechanisms

Genetic Algorithms
SGA technical summary tableau
Representation Binary strings
Recombination N-point or uniform
Mutation Bitwise bit-flipping with fixed
probability
Parent selection Fitness-Proportionate
Survivor selection All children replace parents
Speciality Emphasis on crossover

Genetic Algorithms
Genotype space =
{0,1}L
Phenotype space
Encoding
(representation)
Decoding
(inverse representation)
011101001
010001001
10010010
10010001
Representation

Genetic Algorithms
SGA reproduction cycle
1. Select parents for the mating pool
(size of mating pool = population size)
2. Shuffle the mating pool
3. For each consecutive pair apply crossover with
probability pc , otherwise copy parents
4. For each offspring apply mutation (bit-flip with
probability pm independently for each bit)
5. Replace the whole population with the resulting
offspring

Genetic Algorithms
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)

Genetic Algorithms
SGA operators: mutation
 Alter each gene independently with a probability pm
 pm is called the mutation rate
– Typically between 1/pop_size and 1/ chromosome_length

Genetic Algorithms
 Main idea: better individuals get higher chance
– Chances proportional to fitness
– Implementation: roulette wheel technique
 Assign to each individual a part of the
roulette wheel
 Spin the wheel n times to select n
individuals
SGA operators: Selection
fitness(A) = 3
fitness(B) = 1
fitness(C) = 2
A C
1/6 = 17%
3/6 = 50%
B
2/6 = 33%

Genetic Algorithms
An example after Goldberg ‘89 (1)
 Simple problem: max x2 over {0,1,…,31}
 GA approach:
– Representation: binary code, e.g. 01101  13
– Population size: 4
– 1-point xover, bitwise mutation
– Roulette wheel selection
– Random initialisation
 We show one generational cycle done by hand

Genetic Algorithms
x2 example: selection

Genetic Algorithms
X2 example: crossover

Genetic Algorithms
X2 example: mutation

Genetic Algorithms
The simple GA
 Has been subject of many (early) studies
– still often used as benchmark for novel GAs
 Shows many shortcomings, e.g.
– Representation is too restrictive
– Mutation & crossovers only applicable for bit-string &
integer representations
– Selection mechanism sensitive for converging
populations with close fitness values
– Generational population model (step 5 in SGA repr.
cycle) can be improved with explicit survivor selection

Genetic Algorithms
Alternative Crossover Operators
 Performance with 1 Point Crossover depends on the
order that variables occur in the representation
– more likely to keep together genes that are near
each other
– Can never keep together genes from opposite ends
of string
– This is known as Positional Bias
– Can be exploited if we know about the structure of
our problem, but this is not usually the case

Genetic Algorithms
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)

Genetic Algorithms
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

Genetic Algorithms
Crossover OR mutation?
 Decade long debate: which one is better / necessary /
main-background
 Answer (at least, rather wide agreement):
– it depends on the problem, but
– in general, it is good to have both
– both have another role
– mutation-only-EA is possible, xover-only-EA would not work

Genetic Algorithms
Exploration: Discovering promising areas in the search
space, i.e. gaining information on the problem
Exploitation: Optimising within a promising area, i.e. using
information
There is co-operation AND competition between them
 Crossover is explorative, it makes a big jump to an area
somewhere “in between” two (parent) areas
 Mutation is exploitative, it creates random small
diversions, thereby staying near (in the area of ) the parent
Crossover OR mutation? (cont’d)

Genetic Algorithms
 Only crossover can combine information from two
parents
 Only mutation can introduce new information (alleles)
 Crossover does not change the allele frequencies of
the population (thought experiment: 50% 0’s on first
bit in the population, ?% after performing n
crossovers)
 To hit the optimum you often need a ‘lucky’ mutation
Crossover OR mutation? (cont’d)

Genetic Algorithms
Other representations
 Gray coding of integers (still binary chromosomes)
– Gray coding is a mapping that means that small changes in
the genotype cause small changes in the phenotype (unlike
binary coding). “Smoother” genotype-phenotype mapping
makes life easier for the GA
Nowadays it is generally accepted that it is better to
encode numerical variables directly as
 Integers
 Floating point variables

Genetic Algorithms
Integer representations
 Some problems naturally have integer variables, e.g.
image processing parameters
 Others take categorical values from a fixed set e.g.
{blue, green, yellow, pink}
 N-point / uniform crossover operators work
 Extend bit-flipping mutation to make
– “creep” i.e. more likely to move to similar value
– Random choice (esp. categorical variables)
– For ordinal problems, it is hard to know correct range for
creep, so often use two mutation operators in tandem

Genetic Algorithms
Real valued problems
 Many problems occur as real valued problems, e.g.
continuous parameter optimisation f :  n  
 Illustration: Ackley’s function (often used in EC)

Genetic Algorithms
Mapping real values on bit strings
z  [x,y]   represented by {a1,…,aL}  {0,1}L
• [x,y]  {0,1}L must be invertible (one phenotype per
genotype)
• : {0,1}L  [x,y] defines the representation
 Only 2L values out of infinite are represented
 L determines possible maximum precision of solution
 High precision  long chromosomes (slow evolution)
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
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

Genetic Algorithms
Floating point mutations 1
General scheme of floating point mutations
 Uniform mutation:
 Analogous to bit-flipping (binary) or random resetting
(integers)
l
l x
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Genetic Algorithms
Floating point mutations 2
 Non-uniform mutations:
– Many methods proposed,such as time-varying
range of change etc.
– Most schemes are probabilistic but usually only
make a small change to value
– Most common method is to add random deviate to
each variable separately, taken from N(0, )
Gaussian distribution and then curtail to range
– Standard deviation  controls amount of change
(2/3 of deviations will lie in range (-  to + )

Genetic Algorithms
Crossover operators for real valued GAs
 Discrete:
– each allele value in offspring z comes from one of its
parents (x,y) with equal probability: zi = xi or yi
– Could use n-point or uniform
 Intermediate
– exploits idea of creating children “between” parents
(hence a.k.a. arithmetic recombination)
– zi =  xi + (1 - ) yi where  : 0    1.
– The parameter  can be:
• constant: uniform arithmetical crossover
• variable (e.g. depend on the age of the population)
• picked at random every time

Genetic Algorithms
Single arithmetic crossover
• Parents: x1,…,xn  and y1,…,yn
• Pick a single gene (k) at random,
• child1 is:
• reverse for other child. e.g. with  = 0.5
n
k
k
k x
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y
x
x ...,
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1
(
,
...,
,
1 

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

Genetic Algorithms
Simple arithmetic crossover
• Pick random gene (k) after this point mix values
• child1 is:
n
x
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Genetic Algorithms
• Most commonly used
• child1 is:
Whole arithmetic crossover
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1
(

Genetic Algorithms
Permutation Representations
 Ordering/sequencing problems form a special type
 Task is (or can be solved by) arranging some objects in
a certain order
– Example: sort algorithm: important thing is which elements
occur before others (order)
– Example: Travelling Salesman Problem (TSP) : important thing
is which elements occur next to each other (adjacency)
 These problems are generally expressed as a
permutation:
– if there are n variables then the representation is as a list of n
integers, each of which occurs exactly once

Genetic Algorithms
Permutation representation: TSP example
 Problem:
• Given n cities
• Find a complete tour with
minimal length
 Encoding:
• Label the cities 1, 2, … , n
• One complete tour is one
permutation (e.g. for n =4
[1,2,3,4], [3,4,2,1] are OK)
 Search space is BIG:
for 30 cities there are 30!  1032
possible tours

Genetic Algorithms
Mutation operators for permutations
 Normal mutation operators lead to inadmissible
solutions
– e.g. bit-wise mutation : let gene i have value j
– changing to some other value k would mean that k
occurred twice and j no longer occurred
 Therefore must change at least two values
 Mutation parameter now reflects the probability
that some operator is applied once to the
whole string, rather than individually in each
position

Genetic Algorithms
Insert Mutation for permutations
 Pick two allele values at random
 Move the second to follow the first, shifting the
rest along to accommodate
 Note that this preserves most of the order and
the adjacency information

Genetic Algorithms
Swap mutation for permutations
 Pick two alleles at random and swap their
positions
 Preserves most of adjacency information (4
links broken), disrupts order more

Genetic Algorithms
Inversion mutation for permutations
 Pick two alleles at random and then invert the
substring between them.
 Preserves most adjacency information (only
breaks two links) but disruptive of order
information

Genetic Algorithms
Scramble mutation for permutations
 Pick a subset of genes at random
 Randomly rearrange the alleles in those
positions
(note subset does not have to be contiguous)

Genetic Algorithms
 “Normal” crossover operators will often lead to
inadmissible solutions
 Many specialised operators have been devised
which focus on combining order or adjacency
information from the two parents
Crossover operators for permutations
1 2 3 4 5
5 4 3 2 1
1 2 3 2 1
5 4 3 4 5

Genetic Algorithms
Order 1 crossover
 Idea is to preserve relative order that elements occur
 Informal procedure:
1. Choose an arbitrary part from the first parent
2. Copy this part to the first child
3. Copy the numbers that are not in the first part, to
the first child:
 starting right from cut point of the copied part,
 using the order of the second parent
 and wrapping around at the end
4. Analogous for the second child, with parent roles
reversed

Genetic Algorithms
Order 1 crossover example
 Copy randomly selected set from first parent
 Copy rest from second parent in order 1,9,3,8,2

Genetic Algorithms
Informal procedure for parents P1 and P2:
1. Choose random segment and copy it from P1
2. Starting from the first crossover point look for elements in that
segment of P2 that have not been copied
3. For each of these i look in the offspring to see what element j has
been copied in its place from P1
4. Place i into the position occupied j in P2, since we know that we will
not be putting j there (as is already in offspring)
5. If the place occupied by j in P2 has already been filled in the
offspring k, put i in the position occupied by k in P2
6. Having dealt with the elements from the crossover segment, the rest
of the offspring can be filled from P2.
Second child is created analogously
Partially Mapped Crossover (PMX)

Genetic Algorithms
PMX example
 Step 1
 Step 2
 Step 3

Genetic Algorithms
Cycle crossover
Basic idea:
Each allele comes from one parent together with its position.
Informal procedure:
1. Make a cycle of alleles from P1 in the following way.
(a) Start with the first allele of P1.
(b) Look at the allele at the same position in P2.
(c) Go to the position with the same allele in P1.
(d) Add this allele to the cycle.
(e) Repeat step b through d until you arrive at the first allele of P1.
2. Put the alleles of the cycle in the first child on the positions
they have in the first parent.
3. Take next cycle from second parent

Genetic Algorithms
Cycle crossover example
 Step 1: identify cycles
 Step 2: copy alternate cycles into offspring

Genetic Algorithms
Edge Recombination
 Works by constructing a table listing which
edges are present in the two parents, if an
edge is common to both, mark with a +
 e.g. [1 2 3 4 5 6 7 8 9] and [9 3 7 8 2 6 5 1 4]

Genetic Algorithms
Edge Recombination 2
Informal procedure once edge table is constructed
1. Pick an initial element at random and put it in the offspring
2. Set the variable current element = entry
3. Remove all references to current element from the table
4. Examine list for current element:
– If there is a common edge, pick that to be next element
– Otherwise pick the entry in the list which itself has the shortest list
– Ties are split at random
5. In the case of reaching an empty list:
– Examine the other end of the offspring is for extension
– Otherwise a new element is chosen at random

Genetic Algorithms
Edge Recombination example

Genetic Algorithms
Multiparent recombination
 Recall that we are not constricted by the practicalities
of nature
 Noting that mutation uses 1 parent, and “traditional”
crossover 2, the extension to a>2 is natural to examine
 Been around since 1960s, still rare but studies indicate
useful
 Three main types:
– Based on allele frequencies, e.g., p-sexual voting generalising
uniform crossover
– Based on segmentation and recombination of the parents, e.g.,
diagonal crossover generalising n-point crossover
– Based on numerical operations on real-valued alleles, e.g.,
center of mass crossover, generalising arithmetic
recombination operators

Genetic Algorithms
Population Models
 SGA uses a Generational model:
– each individual survives for exactly one generation
– the entire set of parents is replaced by the offspring
 At the other end of the scale are Steady-State
models:
– one offspring is generated per generation,
– one member of population replaced,
 Generation Gap
– the proportion of the population replaced
– 1.0 for GGA, 1/pop_size for SSGA

Genetic Algorithms
Fitness Based Competition
 Selection can occur in two places:
– Selection from current generation to take part in
mating (parent selection)
– Selection from parents + offspring to go into next
generation (survivor selection)
 Selection operators work on whole individual
– i.e. they are representation-independent
 Distinction between selection
– operators: define selection probabilities
– algorithms: define how probabilities are implemented

Genetic Algorithms
Implementation example: SGA
 Expected number of copies of an individual i
E( ni ) =  • f(i)/ f
( = pop.size, f(i) = fitness of i, f avg. fitness in pop.)
 Roulette wheel algorithm:
– Given a probability distribution, spin a 1-armed
wheel n times to make n selections
– No guarantees on actual value of ni
 Baker’s SUS algorithm:
– n evenly spaced arms on wheel and spin once
– Guarantees floor(E( ni ) )  ni  ceil(E( ni ) )

Genetic Algorithms
 Problems include
– One highly fit member can rapidly take over if rest of
population is much less fit: Premature Convergence
– At end of runs when fitnesses are similar, lose
selection pressure
– Highly susceptible to function transposition
 Scaling can fix last two problems
– Windowing: f’(i) = f(i) -  t
 where  is worst fitness in this (last n) generations
– Sigma Scaling: f’(i) = max( f(i) – ( f  - c • f ), 0.0)
 where c is a constant, usually 2.0
Fitness-Proportionate Selection

Genetic Algorithms
Function transposition for FPS

Genetic Algorithms
Rank – Based Selection
 Attempt to remove problems of FPS by basing
selection probabilities on relative rather than
absolute fitness
 Rank population according to fitness and then
base selection probabilities on rank where
fittest has rank  and worst rank 1
 This imposes a sorting overhead on the
algorithm, but this is usually negligible
compared to the fitness evaluation time

Genetic Algorithms
Linear Ranking
 Parameterised by factor s: 1.0 < s  2.0
– measures advantage of best individual
– in GGA this is the number of children allotted to it
 Simple 3 member example

Genetic Algorithms
Exponential Ranking
 Linear Ranking is limited to selection pressure
 Exponential Ranking can allocate more than 2
copies to fittest individual
 Normalise constant factor c according to
population size

Genetic Algorithms
Tournament Selection
 All methods above rely on global population
statistics
– Could be a bottleneck esp. on parallel machines
– Relies on presence of external fitness function
which might not exist: e.g. evolving game players
 Informal Procedure:
– Pick k members at random then select the best of
these
– Repeat to select more individuals

Genetic Algorithms
Tournament Selection 2
 Probability of selecting i will depend on:
– Rank of i
– Size of sample k
 higher k increases selection pressure
– Whether contestants are picked with replacement
 Picking without replacement increases selection pressure
– Whether fittest contestant always wins
(deterministic) or this happens with probability p
 For k = 2, time for fittest individual to take over
population is the same as linear ranking with s = 2 • p

Genetic Algorithms
Survivor Selection
 Most of methods above used for parent
selection
 Survivor selection can be divided into two
approaches:
– Age-Based Selection
 e.g. SGA
 In SSGA can implement as “delete-random” (not
recommended) or as first-in-first-out (a.k.a. delete-oldest)
– Fitness-Based Selection
 Using one of the methods above or

Genetic Algorithms
Two Special Cases
 Elitism
– Widely used in both population models (GGA,
SSGA)
– Always keep at least one copy of the fittest solution
so far
 GENITOR: a.k.a. “delete-worst”
– From Whitley’s original Steady-State algorithm (he
also used linear ranking for parent selection)
– Rapid takeover : use with large populations or “no
duplicates” policy

Genetic Algorithms
Example application of order based GAs: JSSP
Precedence constrained job shop scheduling problem
 J is a set of jobs.
 O is a set of operations
 M is a set of machines
 Able  O  M defines which machines can perform which
operations
 Pre  O  O defines which operation should precede which
 Dur :  O  M  IR defines the duration of o  O on m  M
The goal is now to find a schedule that is:
 Complete: all jobs are scheduled
 Correct: all conditions defined by Able and Pre are satisfied
 Optimal: the total duration of the schedule is minimal

Genetic Algorithms
Precedence constrained job shop scheduling GA
 Representation: individuals are permutations of operations
 Permutations are decoded to schedules by a decoding procedure
– take the first (next) operation from the individual
– look up its machine (here we assume there is only one)
– assign the earliest possible starting time on this machine, subject to
 machine occupation
 precedence relations holding for this operation in the schedule created so far
 fitness of a permutation is the duration of the corresponding
schedule (to be minimized)
 use any suitable mutation and crossover
 use roulette wheel parent selection on inverse fitness
 Generational GA model for survivor selection
 use random initialisation

Genetic Algorithms
JSSP example: operator comparison

Genetic_Algorithms_genetic for_data .ppt

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