Introduction to Genetic Algorithms

Department of Information Technology 1Soft Computing (ITC4256 )
Dr. C.V. Suresh Babu
Professor
Department of IT
Hindustan Institute of Science & Technology
Introduction to Genetic Algorithms

Action Plan
• What is Genetic Algorithms?
• Introduction to Genetic Algorithm
• Classes of Search Techniques
• Components of a GA
• Components of a GA
• Simple Genetic Algorithm
• GA Cycle of Reproduction
• Population
• Reproduction
• Chromosome Modification: Mutation, Crossover, Evaluation, Deletion
• Example
• GA Technology
• Issues for GA Practitioners
• Benefits of Genetic Algorithms
• GA Application Types
• Quiz

“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
What is Genetic Algorithms?

Introduction to Genetic Algorithm
• Directed search algorithms based on the mechanics of
biological evolution
• Developed by John Holland, University of Michigan (1970’s)
– To understand the adaptive processes of natural systems
– To design artificial systems software that retains the robustness of
natural systems

Introduction Genetic Algorithm (cont.)
• Provide efficient, effective techniques for optimization and
machine learning applications
• Widely-used today in business, scientific and engineering
circles

Classes of Search Techniques
Finonacci Newton
Direct methods Indirect methods
Calculus-based techniques
Evolutionary strategies
Centralized Distributed
Parallel
Steady-state Generational
Sequential
Genetic algorithms
Evolutionary algorithms Simulated annealing
Guided random search techniques
Dynamic programming
Enumerative techniques
Search techniques

Components of a GA
A problem to solve, and ...
• Encoding technique (gene, chromosome)
• Initialization procedure (creation)
• Evaluation function (environment)
• Selection of parents (reproduction)
• Genetic operators (mutation, recombination)
• Parameter settings (practice and art)

Simple Genetic Algorithm
{
initialize population;
evaluate population;
while TerminationCriteriaNotSatisfied
{
select parents for reproduction;
perform recombination and mutation;
evaluate population;
}
}

The GA Cycle of Reproduction
reproduction
population evaluation
modification
discard
deleted
members
parents
children
modified
children
evaluated children

Population
Chromosomes could be:
– Bit strings (0101 ... 1100)
– Real numbers (43.2 -33.1 ... 0.0 89.2)
– Permutations of element (E11 E3 E7 ... E1 E15)
– Lists of rules (R1 R2 R3 ... R22 R23)
– Program elements (genetic programming)
– ... any data structure ...
population

Reproduction
reproduction
population
parents
children
Parents are selected at random with
selection chances biased in relation to
chromosome evaluations.

Chromosome Modification
modification
children
• Modifications are stochastically triggered
• Operator types are:
– Mutation
– Crossover (recombination)
modified children

Mutation: Local Modification
Before: (1 0 1 1 0 1 1 0)
After: (0 1 1 0 0 1 1 0)
Before: (1.38 -69.4 326.44 0.1)
After: (1.38 -67.5 326.44 0.1)
• Causes movement in the search space
(local or global)
• Restores lost information to the population

Crossover: Recombination
P1 (0 1 1 0 1 0 0 0) (0 1 0 0 1 0 0 0) C1
P2 (1 1 0 1 1 0 1 0) (1 1 1 1 1 0 1 0) C2
Crossover is a critical feature of genetic
algorithms:
– It greatly accelerates search early in evolution of a population
– It leads to effective combination of schemata (subsolutions on
different chromosomes)
*

Evaluation
• The evaluator decodes a chromosome and assigns it a fitness measure
• The evaluator is the only link between a classical GA and the problem it
is solving
evaluation
evaluated
children
modified
children

Deletion
• Generational GA:
entire populations replaced with each iteration
• Steady-state GA:
a few members replaced each generation
population
discard
discarded members

An Abstract Example
Distribution of Individuals in Generation 0
Distribution of Individuals in Generation N

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 5) Beijing 7) Tokyo
2) Venice 4) Singapore 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 (2 5 7 2 1 6 3 4)
This operator is called the Order1 crossover.

Mutation involves reordering of the list:
* *
Before: (5 8 7 2 1 6 3 4)
After: (5 8 6 2 1 7 3 4)
Mutation

TSP Example: 30 Cities
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x

Solution i (Distance = 941)
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 (Performance = 941)

Solution j(Distance = 800)
44
62
69
67
78
64
62
54
42
50
40
40
38
21
35
67
60
60
40
42
50
99
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x

Solution k(Distance = 652)
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x

Best Solution (Distance = 420)
42
38
35
26
21
35
32
7
38
46
44
58
60
69
76
78
71
69
67
62
84
94
0
20
40
60
80
100
120
0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 Solution (Performance = 420)

Overview of Performance
0
200
400
600
800
1000
1200
1400
1600
1800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31
D
is
ta
n
c
e
Generations (1000)
TSP30 - Overview of Performance
Best
Worst
Average

Considering the GA Technology
“Almost eight years ago ...
people at Microsoft wrote
a program [that] uses
some genetic things for
finding short code
sequences. Windows 2.0
and 3.2, NT, and almost all
Microsoft applications
products have shipped
with pieces of code created
by that system.”
- Nathan Myhrvold, Microsoft Advanced
Technology Group, Wired, September 1995

Issues for GA Practitioners
• Choosing basic implementation issues:
– representation
– population size, mutation rate, ...
– selection, deletion policies
– crossover, mutation operators
• Termination Criteria
• Performance, scalability
• Solution is only as good as the evaluation function (often hardest part)

Benefits of Genetic Algorithms
• Concept is easy to understand
• Modular, separate from application
• Supports multi-objective optimization
• Good for “noisy” environments
• Always an answer; answer gets better with time
• Inherently parallel; easily distributed

Benefits of Genetic Algorithms (cont.)
• Many ways to speed up and improve a GA-based application as
knowledge about problem domain is gained
• Easy to exploit previous or alternate solutions
• Flexible building blocks for hybrid applications
• Substantial history and range of use

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 semiconductor layout, aircraft design, keyboard
configuration, communication networks
Scheduling manufacturing, facility scheduling, resource allocation
Robotics trajectory planning
Machine Learning designing neural networks, improving classification
algorithms, classifier systems
Signal Processing 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

Test Yourself
1. Genetic Algorithm are a part of
a. Evolutionary Computing
b. inspired by Darwin's theory about evolution - "survival of the fittest"
c. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics
d. All of the above
2. Which of the following are discrete optimization problems?
a. Travelling salesman problem
b. Robot control
c. Chess playing program
d. Prediction of stock prices
3. Biologically inspired computations appropriate for
a. Optimization
b. Modelling
c. Safety critical systems
d. Simulation
4. Exploration in search is
a. Concerned with improving the current best solution by local search
b. Combined with exploitation in evolutionary algorithms
c. Often resulting in getting stuck in local optima
d. Concerned with global search
5. Evolutionary algorithm :Initialization
a. Individuals are normally generated randomly
b. Is concerned with generating candidate solutions
c. Mutation of candidates is normally also taking place during the initialization
d. Heuristics for generating candidates can be applied

Answers
1. Genetic Algorithm are a part of
a. Evolutionary Computing
b. inspired by Darwin's theory about evolution - "survival of the fittest"
c. are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics
d. All of the above
2. Which of the following are discrete optimization problems?
a. Travelling salesman problem
b. Robot control
c. Chess playing program
d. Prediction of stock prices
3. Biologically inspired computations appropriate for
a. Optimization
b. Modelling
c. Safety critical systems
d. Simulation
4. Exploration in search is
a. Concerned with improving the current best solution by local search
b. Combined with exploitation in evolutionary algorithms
c. Often resulting in getting stuck in local optima
d. Concerned with global search
5. Evolutionary algorithm :Initialization
a. Individuals are normally generated randomly
b. Is concerned with generating candidate solutions
c. Mutation of candidates is normally also taking place during the initialization
d. Heuristics for generating candidates can be applied

Introduction to Genetic Algorithms

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