Laurent DUMAS
Archives
: course 2009
Course Objectives
:
Many problems occuring in industry consist in minimizing
(or maximizing) a certain cost function. This course is aimed to
present various optimization methods in order to solve such problems.
After a general introduction on numerical optimization,
two different families of optimization methods will be presented:
first, the the stochastic methods (genetic algorithms, evolution
strategies, etc...) and then deterministic descent-type methods (gradient,
newton, etc…). A numerical implementation with Scilab of these differents
methods will be done during the computer sessions, before being applied
for the resolution of applicative problems
Course prerequisites
:
No specific prerequisites are needed for this course.
It is accessible with basic tools in analysis (functions
of n variables).
Syllabus
(électif 11):
Mercredi 17 mars Cours 14-17h: introduction
and examples
Mercredi 24 mars Cours 14-17h:
deterministic methods: descent methods (steepest gradient)+computer session
Vendredi 26 mars Cours 8h-11h:
deterministic methods: descent methods (BFGS) + computer session:
BFGS-banana.sci
Mercredi 31 mars Cours 14-17h:
binary genetic algorithm+ computer session
GA-binary2010.sci
Mercredi 05 mai Cours 14-17h:
real valued genetic algorithm+ computer session
GA-real2010.sci
Mardi 11 mai Cours
8h-11h: evolution strategies, adaptativity principles + computer session
ES-CSA2010.sci
Vendredi 14 mai Cours 8h-11h: PSO,
DE, constraints handling, robust optimization+ computer session
ES-CSA2010-can.sci
Mercredi 26 mai Cours 14-17h: multi-objective
optimization NSGA-2010.sci
Vendredi 28 mai Cours 14-17h:
examples in industry and medicine
Exam: 09 juin: énoncé
Rattapage: 31 Aout: énoncé
References
:
Numercial optimization, theoretical and practical aspects
: JF Bonnans, JC Gilbert, C. Lemaréchal, C. Sagastizbal,
Springer Verlag 2003.
Genetic Algorithms on search, optimization and machine learning
: D. Goldberg, 1989
Multi-Objective Optimization Using Evolutionary Algorithms
, K. Deb, 2001
On the web
:
Theoretical part:
An online course on '
Numerical Optimization
' at Oxford University
An online course on '
Optimization inengineering design
' at the Georgia Institute of Technology
A book
Numerical recipes in C or Fortran 77
(Chapter 10 mainly)
An introduction article on '
Numerical Solution of Optimization Test-Cases by Genetic Algorithms
' by N. Marco and J.A. Desideri
Applicative part:
The I-beam problem:
Description of the I-Beam problem
The FBG problem: a talk '
Optimisation of optical communication systems by means of
genetic algorithms'
by myself
The FBG problem: a PhD
dissertation “
Synthesis and characterization of fiber Bragg gratings
” by J. Skaar (chapters 2 and 3.1 mainly)
The FBG problem: an article
'Real-coded
genetic algorithm for Bragg grating parameter synthesis'
by G. Cormier and R. Boudreau
The FBG problem: an article
'
Multi-objective and constrained design of fibre Bragg gratings
using evolutionary algorithms
' by S. Manos and L. Poladian
The LJ problem:
article 1
article
2
,
article 3
, article
4
thesis