Genetic Algorithms and Evolutionary Computing

cathamΤεχνίτη Νοημοσύνη και Ρομποτική

23 Οκτ 2013 (πριν από 3 χρόνια και 11 μήνες)

63 εμφανίσεις

H02D1A

Genetic

Algorithms

and

Evolutionary

Computing



Dirk
Roose


1d semester; 4 credit
points


Aims


to

describe

genetic

algorithms

and

(
other
)
evolutionary

strategies

for

search
and

optimisation



to

analyse
their

performance

(
quality

of
results
,
computational

cost
)


to

discuss

some

implementation

issues


to

illustrate

the
methods

by

solving

some

model
problems

(e.g.
travelling

salesman
problem
, transportation
problem
)


to

present
some

case studies

(e.g. concept
learning
,
timetabling
,

artificial

life’)



the
student

will

be

able

to

decide

whether

these
methods

are
suited

to

solve

a
particular

search or
optimisation

problem
,
and

how

to

choose

the
appropriate

methods

/
genetic

operators


Genetic

Algorithms

and

Evolutionary

Computing


Prerequisite

basic
(
bachelor
)
courses in
informatics

(
programming
,
algorithms
)

and

mathematics

(analysis,
statistics
)


Course
Material


s
ome
chapters from
Genetic Algorithms and Genetic Programming.
Modern Concepts and Practical Applications. M.
Affenzeller
, S.
Winkler, S. Wagner, and A.
Beham
, Chapman and Hall/CRC 2009
(book ;
e
-
book
)


s
ome
papers


Teaching
activities


lectures

: 12 x 1.5
hour


last
lectures
: short
presentations

by

students

about

ongoing

project





(incl.
i
nteresting

or
unexpected

results
,
questions
)


exercises

& practical
sessions

:
4
x 2.5h =
10 h


Project



experiments
with
Matlab

code,
groups

of 2
students
,
±

40
hour
s


Exam


open
book

exam

(
theory

&
exercises
)
incl
.
discussion on
project report

Genetic

Algorithms

and

Evolutionary

Computing


Lectures

:

Wednesday

9
am

Celestijnenlaan
200D (Physics building), room
05.11



Exercises:

2 groups; on Mondays & Tuesdays

H03F9A

Parallel computing

Dirk Roose & Albert
-
Jan
Yzelman

1d
semester


Aims

I
nsight

in


parallel
computers
and

available

software

environments,


t
he design and
performance analysis

of

parallel
algorithms
.


The student
will

be

able

to


d
esign

efficient

parallel
versions

of
algorithms

with

simple

data
dependencies


b
oth

in the ‘shared
address

space

programming

model
and

in the

message

passing’
programming

model.


KU Leuven HPC
Cluster
with

2736 ’cores’

6

Revolution is Happening Now


Chip density is
continuing to increase

~2x every 2 years


Clock speed is not


Number of
processor cores
may double
instead


There is little or no
hidden parallelism
(ILP) to be found


Parallelism must be
exposed to and
managed by software

Source: Intel, Microsoft (Sutter) and
Stanford (
Olukotun
, Hammond)

Parallel
computing:
Content


Architecture of parallel HPC systems (short)


Performance
analysis
on parallel systems


Design
and
analysis of parallel
algorithms

for

model
problems




(matrix operations,
sorting
,
fast

Fourier

transform
)
using

the

BSP model



S
imple
examples

in
BSPlib
,
BSPonMPI
,
MulticoreBSP



(MPI: Message Passing Interface)



Dynamic

load
-
balancing


Guest

lecture

on Parallel Matching
by

Rob
Bisseling








Prerequisites

Bachelor
-
level
knowledge

of
algorithms

&
programming

Course
material

Rob H.
Bisseling
,
Parallel Scientific Computation.

A Structured Approach using BSP and MPI
.

Oxford University Press, 2004
.

+
some

papers

Exercises

and

practical
sessions

5
sessions

(
3
on
parallel systems:



multicore

processor; HPC
-
cluster)

no
project

Exam

Open
book

exam


insight

in
theory

(in
particular

performance analysis)


design of
an

efficient

parallel
algorithm

(high level
description
)

Parallel
computing

9

Exascience

Lab


see
www.exascience.com