Tutorial 5
Q1.
Consider a population of chromosomes in a genetic algorithm with their fitness values
specified as below:
Chromosome
A
11100101
B
11001100
C
11000101
D
01100111
E
11100110
F
00101110
Fi
Part 4 : Modifications on
Simple GA
City University of Hong Kong
Simple Genetic Algorithm
Binary representation
Roulette Wheel Selection
Single Point Crossover
Bit Mutation
High crossover rate and
low
Part 3: Theory and Hypothesis
City University of Hong Kong
Mathematical Models for GA
Macroscopic models
Focus on the
properties of a
large set of
individuals
Microscopic models
Focus on the
properti
Part 2: Basic Genetic Algorithm
City University of Hong Kong
Genetic Algorithm
Natural selection:
Survival of the fittest
DNA structures
A
C
T
G
Nucleotides
AAA
CGA
ATC
Codons
. A A A C G A A T C .
Part 1:
Optimization - Problems and Classical Methods
City University of Hong Kong
Optimization Problems
Find the best solution from all feasible
solutions for a problem
Best for a function, cost,
Ex
This supplementary note is to further explain the example given in page 8 in Part 4.
Consider schemata with order 3, the disruptive rate is 0.75 with a uniform crossover operation.
For one-point cross
Answer of Tutorial 5
6
7
Q1.
The derated fitness of chromosome A = 3
Q2.
Note: In this question, the Euclidean distance is used and it can be computed by:
d ij = ( xi x j ) 2 + ( y i y j ) 2 + ( z i z
Answer of Tutorial 4
Q1. S p =
Ts 10000
=
= 8.696
T p 1150
Q2. (a, b)
Chromosome
A
(a) Fonseca-Flemings rank
5 (B,D,E,F are better than A)
B
3 (D,F are better than B)
C
3 (D,E are better than C)
D
E
F
Part 5: Advantages
City University of Hong Kong
Strength of GA
Handle multi-modal problems
Handle multi-objective problems
Parallelism
Handle constrained problems
Multi-objective Problems
Linear combi
Pareto-based fitness assignment
(Fonseca and Fleming) with goal
Case 3: F(Ia) partially meets the goals V
Without loss of generality, let
k [1, m) , i 1, , k , j (k 1), , m
f i ( I a ) vi f j ( I a )
Part 6: Problems and Difficulties
City University of Hong Kong
Problem 1:
Premature Convergence and Genetic Drift
Stochastic errors in sampling caused by small population
sizes
Genetic Drift: Popula
Tutorial 4
Q1.
Consider a Farmer-and-Workers Model for a parallel genetic algorithm. Assuming that
there are 100 offspring to be evaluated in each generation, and there are 10 processing
units (1 Farm
Tutorial 3
Q1.
Consider the following two parents (each represented by a chromosome in 12-bits):
0100 0100 1111
1100 1101 0011
Construct the two offspring by
(a) performing the one-point crossover wit
Tutorial 2
Q1.
What are the order and the defining length of the following schemata?
(a)
(b)
(c)
Q2.
10*00*110
*00101*1
000111000111
Consider a chromosome constructed as a string of 5 bits,
(a)
(b)
(c
Tutorial 1
Q1.
Describe the genetic cycle for a conventional genetic algorithm.
Q2. Consider a population of 4 chromosomes with their fitness specified in the table.
Chromosome
A
B
C
D
Fitness
10
4
1
Pseudo code of Migration GA
initialize P demes of size N each
generation = 1
while (NOT terminated)
cfw_
for each deme /* do in parallel */
cfw_
/* migration */
if mod(generation, frequency) = 0
cfw_
Course:
EE4047 - Genetic Algorithms and Their Applications
Objectives
1. To let students be familiar with the GA concept and procedures
2. To visualize the effect of parameter setting on the performan
Part 7: Advanced Designs in GA
(I) Hybrid Design
City University of Hong Kong
Example: Cloth Cutting
fabric
Used
length
r4
r1
r2
Rectangular
pieces
r3
Objective
Fabric in a long roll
Cloth cutting = 2
Answer of Tutorial 1 Q1. You are to describe the genetic cycle (shown below) in word
Population (chromosomes)
PhenoType
Selection
Fitness
Replacement
M ating Pool (parents)
Objective Function
Genetic
Test Questions in Past Few Years Question 5 The yellow region is for the goals. Those meet all the goals will fall inside.
To build the table using Fonseca and Fleming approach with goal attainment, y
Part 8: Advanced GA Designs
(I) Hierarchical Chromosome Structure
City University of Hong Kong
Biological Inspiration
Regulatory Sequences and Structural Genes
tran-acting factor transcription initiat
Part 7: Problems and Difficulties
City University of Hong Kong
Problem 1:
Premature Convergence and Genetic Drift
Stochastic errors in sampling caused by small population sizes Genetic Drift: Populati
Part 6: Advantages
City University of Hong Kong
Strength of GA
Handle multi-modal problems Handle multi-objective problems Parallelism Handle constrainted problems
1
Multi-objective Problems
Linear co
Part 5: Modification on Simple GA
City University of Hong Kong
Simple Genetic Algorithm
Binary representation Roulette Wheel Selection Single Point Crossover Bit Mutation High crossover rate and low
Part 4: Theory and Hypothesis
City University of Hong Kong
Hyperplanes
Assume that we have a problem, where the solution can be encoded in 3 bits (X1,X2,X3)
X2
010 110
011 000 001
111 100
X1
101
X3
*1
Part 2: Basic Genetic Algorithm
City University of Hong Kong
Genetic Algorithm
Natural selection: Survival of the fittest DNA structures
A C T G Nucleotides AAA CGA Codons . A A A C G A A T C . Genes
Part 1: Optimization Problems and Classical Methods
City University of Hong Kong
Optimization Problems
Find the best solution from all feasible solutions for a problem Best for a function, cost, Examp