Evolutionary- Algorithms by chandrapro

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```									        Introduction to Evolutionary
Computation
• Evolutionary Computation is the field of study devoted to
the design, development, and analysis is problem solvers
based on natural selection (simulated evolution).

• Evolution has proven to be a powerful search process.

• Evolutionary Computation has been successfully applied
to a wide range of problems including:
•   Aircraft Design,
•   Routing in Communications Networks,
•   Tracking Windshear,
•   Game Playing (Checkers [Fogel])
1
Introduction to Evolutionary
Computation
(Applications cont.)
•   Robotics,
•   Air Traffic Control,
•   Design,
•   Scheduling,
•   Machine Learning,
•   Pattern Recognition,
•   Job Shop Scheduling,
•   VLSI Circuit Layout,
•   Strike Force Allocation,
2
Introduction to Evolutionary Computation
(Applications cont.)

• Theme Park Tours (Disney Land/World)
http://www.TouringPlans.com
• Market Forecasting,
• Egg Price Forecasting,
• Design of Filters and Barriers,
• Data-Mining,
• User-Mining,
• Resource Allocation,
• Path Planning,
• Etc.

3
Example of Evolutionary Algorithm
• An Example Evolutionary Computation
Procedure EC{
t = 0;
Initialize Pop(t);
Evaluate Pop(t);
While (Not Done)
{
Parents(t) = Select_Parents(Pop(t));
Offspring(t) = Procreate(Parents(t));
Evaluate(Offspring(t));
Pop(t+1)= Replace(Pop(t),Offspring(t));
t = t + 1;
}

4
Candidate Solutions CS

1. In an Evolutionary Computation, a population of candidate
solutions (CSs) is randomly generated.

2. Each of the CSs is evaluated and assigned a fitness based on a
user specified evaluation function.
1.   The evaluation function is used to determine the „goodness‟ of a CS.

3. A number of individuals are then selected to be parents based on
their fitness.

4. The Select_Parents method must be one that balances the urge for
selecting the best performing CSs with the need for population
diversity.

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Parents and Generations
1. The selected parents are then allowed to create a set of
offspring which are evaluated and assigned a fitness
using the same evaluation function defined by the user.

2. Finally, a decision must be made as to which individuals
of the current population and the offspring population
should be allowed to survive.
1.   Typically, in EC , this is done to guarantee that the population
size remains constant.
2.    [The study of ECs with dynamic population sizes would make
an interesting project for this course]

6
Selecting and Stopping

• Once a decision is made the survivors comprise the next generation
(Pop(t+1)).

• This process of selecting parents based on their fitness, allowing
them to create offspring, and replacing weaker members of the
population is repeated for a user specified number of cycles.

• Stopping conditions for evolutionary search could be:
•   The discovery of an optimal or near optimal solution
•   Convergence on a single solution or set of similar solutions,
•   When the EC detects the problem has no feasible solution,
•   After a user-specified threshold has been reached, or
•   After a maximum number of cycles.

7
A Brief History of Evolutionary
Computation
• The idea of using simulated evolution to solve
engineering and design problems have been around
since the 1950‟s (Fogel, 2000).
•   Bremermann, 1962
•   Box, 1957
•   Friedberg, 1958

• However, it wasn‟t until the early 1960‟s that we began
to see three influential forms of EC emerge (Back et al,
1997):
•   Evolutionary Programming (Lawrence Fogel, 1962),
•   Genetic Algorithms (Holland, 1962)
•   Evolution Strategies (Rechenberg, 1965 & Schwefel, 1968),
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A Brief History of Evolutionary Computation
(cont.)

• The designers of each of the EC techniques saw
that their particular problems could be solved via
simulated evolution.

•   Fogel was concerned with solving prediction
problems.

•   Rechenberg & Schwefel were concerned with solving
parameter optimization problems.

•   Holland was concerned with developing robust
A Brief History of Evolutionary Computation
(cont.)

• Each of these researchers successfully developed
appropriate ECs for their particular problems
independently.

• In the US, Genetic Algorithms have become the most
popular EC technique due to a book by David E.
Goldberg (1989) entitled, “Genetic Algorithms in Search,
Optimization & Machine Learning”.

• This book explained the concept of Genetic Search in
such a way the a wide variety of engineers and scientist
could understand and apply.
10
A Brief History of Evolutionary Computation
(cont.)
• However, a number of other books helped fuel the
growing interest in EC:
•   Lawrence Davis’, “Handbook of Genetic Algorithms”,
(1991),
•   Zbigniew Michalewicz’ book (1992), “Genetic Algorithms
+ Data Structures = Evolution Programs.
•   John R. Koza’s “Genetic Programming” (1992), and
•   D. B. Fogel’s 1995 book entitled, “Evolutionary
Computation: Toward a New Philosophy of Machine
Intelligence.

• These books not only fueled interest in EC but they also
were instrumental in bringing together the EP, ES, and
GA concepts together in a way that fostered unity and
an explosion of new and exciting forms of EC.
11
A Brief History of Evolutionary Computation:
The Evolution of Evolutionary Computation

• First Generation EC
•   EP (Fogel)
•   GA (Holland)
•   ES (Rechenberg, Schwefel)

• Second Generation EC
•   Genetic Evolution of Data Structures (Michalewicz)
•   Genetic Evolution of Programs (Koza)
•   Hybrid Genetic Search (Davis)
•   Tabu Search (Glover)

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A Brief History of Evolutionary Computation:
The Evolution of Evolutionary Computation (cont.)

• Third Generation EC
•   Artificial Immune Systems (Forrest)
•   Cultural Algorithms (Reynolds)
•   Ant Colony Optimization (Dorigo)
•   Particle Swarm Optimization (Kennedy & Eberhart)
•   Memetic Algorithms
•   Estimation of Distribution Algorithms
• Fourth Generation ????

13
Introduction to Evolutionary
Computation:
A Simple Example
• Let‟s walk through a simple example!

• Let‟s say you were asked to solve the following problem:
•   Maximize:
•   f6(x,y) = 0.5 + (sin(sqrt(x2+y2))2 – 0.5)/(1.0 + 0.001(x2+y2))2
•   Where x and y are take from [-100.0,100.0]
•   You must find a solution that is greater than 0.99754, and you
can only evaluate a total of 4000 candidate solutions (CSs)

• This seems like a difficult problem.
•   It would be nice if we could see what it looks like!
•   This may help us determine a good algorithm for solving it.
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Introduction to Evolutionary Computation:
A Simple Example
•   A 3D view of f6(x,y):

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Introduction to Evolutionary Computation:
A Simple Example
•   If we just look at only one dimension f6(x,1.0)

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Introduction to Evolutionary Computation:
A Simple Example
• Let‟s develop a simple EC for solving this
problem

• An individual (chromosome or CS)
•   <xi,yi>
•   fiti = f6(xi,yi)

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Introduction to Evolutionary Computation:
A Simple Example
Procedure simpleEC{
t = 0;
Initialize Pop(t); /* of P individuals */
Evaluate Pop(t);
while (t <= 4000-P){
Select_Parent(<xmom,ymom>); /* Randomly */
Create_Offspring(<xkid,ykid>):
xkid = rnd(xmom, xdad) + Nx(0,);
ykid = rnd(ymom, ydad) + Ny(0,);
fitkid = Evaluate(<xkid,ykid>);
Pop(t+1) = Replace(worst,kid);{Pop(t)-{worst}}{kid}
t = t + 1;
}
}
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Introduction to Evolutionary Computation:
A Simple Example
•   To simulate this simple EC we can use the applet at:
•   http://www.eng.auburn.edu/~gvdozier/GA.html

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Introduction to Evolutionary Computation:
A Simple Example

• To get a better understanding of some of
the properties of ECs let‟s do the „in class‟
lab found at:
http://www.eng.auburn.edu/~gvdozier/GA
_Lab.html

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Hill climbing

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Introduction 1
• Inspired by natural evolution
• Population of individuals
•   Individual is feasible solution to problem
• Each individual is characterized by a Fitness function
•   Higher fitness is better solution
• Based on their fitness, parents are selected to reproduce
offspring for a new generation
•   Fitter individuals have more chance to reproduce
•   New generation has same size as old generation; old generation
dies
• Offspring has combination of properties of two parents
• If well designed, population will converge to optimal
solution
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Algorithm
BEGIN
Generate initial population;
Compute fitness of each individual;
REPEAT /* New generation /*
FOR population_size / 2 DO
Select two parents from old generation;
/* biased to the fitter ones */
Recombine parents for two offspring;
Compute fitness of offspring;
Insert offspring in new generation
END FOR
UNTIL population has converged
END
23
Example of convergence

24
Introduction 2
• Reproduction mechanism has no knowledge of the
problem to be solved

• Link between genetic algorithm and problem:
•   Coding
•   Fitness function

25
Basic principles 1
• Coding or Representation
•   String with all parameters
• Fitness function
•   Parent selection
• Reproduction
•   Crossover
•   Mutation
• Convergence
•   When to stop

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Basic principles 2
• An individual is characterized by a set of parameters:
Genes
• The genes are joined into a string: Chromosome

• The chromosome forms the genotype
• The genotype contains all information to construct an
organism: the phenotype

• Reproduction is a “dumb” process on the chromosome of
the genotype
• Fitness is measured in the real world („struggle for life‟)
of the phenotype

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Coding
• Parameters of the solution (genes) are concatenated to
form a string (chromosome)
• All kind of alphabets can be used for a chromosome
(numbers, characters), but generally a binary alphabet is
used
• Order of genes on chromosome can be important
• Generally many different codings for the parameters of a
solution are possible
• Good coding is probably the most important factor for
the performance of a GA
• In many cases many possible chromosomes do not code
for feasible solutions

28
Example of coding for TSP
Travelling Salesman Problem
• Binary
•   Cities are binary coded; chromosome is string of bits
Most chromosomes code for illegal tour
Several chromosomes code for the same tour
• Path
•   Cities are numbered; chromosome is string of integers
Most chromosomes code for illegal tour
Several chromosomes code for the same tour
• Ordinal
•   Cities are numbered, but code is complex
•   All possible chromosomes are legal and only one chromosome
for each tour
• Several others
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Reproduction
• Crossover
•   Two parents produce two offspring
•   There is a chance that the chromosomes of the two parents are
copied unmodified as offspring
•   There is a chance that the chromosomes of the two parents are
randomly recombined (crossover) to form offspring
•   Generally the chance of crossover is between 0.6 and 1.0
• Mutation
•   There is a chance that a gene of a child is changed randomly
•   Generally the chance of mutation is low (e.g. 0.001)

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Crossover
• One-point crossover
• Two-point crossover
• Uniform crossover

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One-point crossover 1
• Randomly one position in the chromosomes is chosen
• Child 1 is head of chromosome of parent 1 with tail of
chromosome of parent 2
• Child 2 is head of 2 with tail of 1
Randomly chosen position

Parents:       1010001110            0011010010

Offspring: 0101010010                0011001110

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One-point crossover 2
1          2     1          2

2          1        2       1
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Two-point crossover
• Randomly two positions in the chromosomes are chosen
• Avoids that genes at the head and genes at the tail of a
chromosome are always split when recombined

Randomly chosen positions

Parents:       1010001110             0011010010

Offspring: 0101010010                 0011001110

34
Uniform crossover
• A random mask is generated
• The mask determines which bits are copied from one
parent and which from the other parent
• Bit density in mask determines how much material is
taken from the other parent (takeover parameter)

Parents:      1010001110        0011010010

Offspring: 0011001010           1010010110

35
Problems with crossover
• Depending on coding, simple crossovers can have high
chance to produce illegal offspring
•   E.g. in TSP with simple binary or path coding, most offspring will
be illegal because not all cities will be in the offspring and some
cities will be there more than once
• Uniform crossover can often be modified to avoid this
problem
•   E.g. in TSP with simple path coding:
Where mask is 1, copy cities from one parent
Where mask is 0, choose the remaining cities in the order of the
other parent

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Fitness Function
Purpose
• Parent selection
• Measure for convergence
• For Steady state: Selection of individuals to die

• Should reflect the value of the chromosome in some
“real” way
• Next to coding the most critical part of a GA

37
Parent selection
Chance to be selected as parent proportional to fitness
• Roulette wheel

To avoid problems with fitness function
• Tournament

Not a very important parameter

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Roulette wheel
• Sum the fitness of all chromosomes, call it T
• Generate a random number N between 1 and T
• Return chromosome whose fitness added to the running
total is equal to or larger than N
• Chance to be selected is exactly proportional to fitness

Chromosome:     1         2      3      4     5      6
Fitness:        8         2      17     7     4      11
Running total: 8          10     27     34    38     49
N (1  N  49):                  23
Selected:                        3

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Tournament
• Binary tournament
•   Two individuals are randomly chosen; the fitter of the two is
selected as a parent
• Probabilistic binary tournament
•   Two individuals are randomly chosen; with a chance p,
0.5<p<1, the fitter of the two is selected as a parent
• Larger tournaments
•   n individuals are randomly chosen; the fittest one is selected as
a parent

• By changing n and/or p, the GA can be adjusted
dynamically

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Problems with fitness range
• Premature convergence
•   Fitness too large
•   Relatively superfit individuals dominate population
•   Population converges to a local maximum
•   Too much exploitation; too few exploration
• Slow finishing
•   Fitness too small
•   No selection pressure
•   After many generations, average fitness has converged, but no
global maximum is found; not sufficient difference between best
and average fitness
•   Too few exploitation; too much exploration

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Solutions for these problems
• Use tournament selection
•   Implicit fitness remapping
• Adjust fitness function for roulette wheel
•   Explicit fitness remapping
Fitness scaling
Fitness windowing
Fitness ranking

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Fitness scaling
• Fitness values are scaled by subtraction and division so
that worst value is close to 0 and the best value is close
to a certain value, typically 2
•   Chance for the most fit individual is 2 times the average
•   Chance for the least fit individual is close to 0
• Problems when the original maximum is very extreme
(super-fit) or when the original minimum is very extreme
(super-unfit)
•   Can be solved by defining a minimum and/or a maximum value
for the fitness

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Example of Fitness Scaling

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Fitness windowing
• Same as window scaling, except the amount subtracted
is the minimum observed in the n previous generations,
with n e.g. 10
• Same problems as with scaling

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Fitness ranking
• Individuals are numbered in order of increasing fitness
• The rank in this order is the adjusted fitness
• Starting number and increment can be chosen in several
ways and influence the results

• No problems with super-fit or super-unfit
• Often superior to scaling and windowing

46
Other parameters of GA 1
• Initialization:
•   Population size
•   Random
•   Dedicated greedy algorithm
• Reproduction:
•   Generational: as described before (insects)
•   Generational with elitism: fixed number of most fit individuals
are copied unmodified into new generation
•   Steady state: two parents are selected to reproduce and two
parents are selected to die; two offspring are immediately
inserted in the pool (mammals)

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Other parameters of GA 2
• Stop criterion:
•   Number of new chromosomes
•   Number of new and unique chromosomes
•   Number of generations
• Measure:
•   Best of population
•   Average of population
• Duplicates
•   Accept all duplicates
•   Avoid too many duplicates, because that degenerates the
population (inteelt)
•   No duplicates at all

48
Example run
Maxima and Averages of steady state and generational
replacement

45        St_max

40        St_av.
Ge_max
35
Ge_av.
30
25
20
15
10
5
0
0             5     10         15         20

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Introduction to Evolutionary Computation:
1.     Bäck, T., Hammel, U., and Schwefel, H.-P. (1997). “Evolutionary
Computation: Comments on the History and Current State,” IEEE
Transactions on Evolutionary Computation, VOL. 1, NO. 1, April
1997.

2.     Spears, W. M., De Jong, K. A., Bäck, T., Fogel, D. B., and de
Garis, H. (1993). “An Overview of Evolutionary Computation,” The
Proceedings of the European Conference on Machine Learning,
v667, pp. 442-459.
(http://www.cs.uwyo.edu/~wspears/papers/ecml93.pdf)

3.     De Jong, Kenneth A., and William M. Spears (1993). “On the
State of Evolutionary Computation”, The Proceedings of the Int'l
Conference on Genetic Algorithms, pp. 618-623.
(http://www.cs.uwyo.edu/~wspears/papers/icga93.pdf)

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