Course on Evolutionary Algorithms
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Teaching activities
Basic information on the course:
course annotation
on Study Programmes pages (look at Intelligent Systems - MS level)
detail information
Academic year 2023/2024
Class time and location
- Summer term
Lectures:
Tuesdays, 10:50-12:20 V010
Practicals:
Wednesdays, 10:50-12:20 V010
Staff
Lectures:
Marian Mach
(faculty)
Practicals:
Marian Mach
(faculty)
Lecture main topics
(for details see presentations in Mind maps section)
Inspiring principles and basic algorithm structure
Basic building blocks of EA
Special usage of EA (niche, multicriterial, non-stationary, constrained)
Practical algorithm setting
Practicals topics
Experiments with algorithm blocks (selection, genetic operators)
Using EA to solve different problem types (~5 problem types)
Applications of EA (1 problem, 1 presentation)
Requirements for successful course completion
Compulsory practical attendance (max 3 absences)
Compulsory lecture attendance (max 3 absences)
To be awarded with overall credit > 50% (credit for practicals > 20%, final exam credit > 30%)
Grading
Practicals: 40%
Final exam: 60%
Basic study literature for the course
Evolutionary algorithms: elements and principles
Evolutionary algorithms: problem solving
Additional material to the course
Lectures' mind maps
Introduction
map as html
map as pdf
obr1.1
obr2.1
Selection
map as html
map as pdf
s91-s92
Replacement
map as html
map as pdf
Representation
map as html
map as pdf
s41-s42
Fitness
map as html
map as pdf
s48
s56-s57
Genetic operators
map as html
map as pdf
obr8.7
obr8.8
Population diversity
map as html
map as pdf
obr10.1
obr10.2
obr10.3-obr10.5
Niche problems
map as html
map as pdf
obr1.3
obr1.4
obr1.5
obr1.6-obr1.9
Multicriterial problems
map as html
map as pdf
obr2.1
obr2.2
obr2.4-obr2.5
obr2.6-obr2.9
Non-stationary problems
map as html
map as pdf
obr3.2
obr3.4
obr3.5-obr3.6
obr3.7-obr3.9
Constrained problems
map as html
map as pdf
obr4.6
obr4.7
obr4.8-obr4.11
Sequencial problems (TSP)
map as html
map as pdf
Schema theorem
map as html
map as pdf
obr11.1
Parameter setting
map as html
map as pdf
obr9.1-obr9.2
Case studies
Optimisation of cell layout in a manufacturing facility
Students' projects
2024
Stable matching:
problem definition
(
data for SM
,
data for SMI
,
data for SMT
,
data for SMTI
),
validator
(
linux executable
)
Car rent facility:
problem definition
Pickup and delivery:
problem definition
(
data for PD
),
validator
(
linux executable
)
Students' experiments
Simple version of EA (search for function extreme)
Bias/spread of selection methods:
experiment notebook
,
data
(Spring Term 2017)
Takeover-time:
experiment notebook
,
Selection drift:
experiment notebook
,
Material reduction by selection methods:
experiment notebook
,
data
(Spring Term 2019)
Search potential of crossover operators:
data
Auxiliary material
Treasure hunting
UI scripts
(
Gnuplot homepage
),
visualisation demo
snapshots:
without solution
,
with solution
expected solutions:
basic version
,
TSP
,
more solutions
(
picture
),
multiobjective
(
picture
),
non-stationary
(
picture
),
constraints
(
picture
),
numerical constraints
(
picture
)
Real problem applications
Path planning optimization of six-degree-of-freedom robotic manipulators
Solving marketing optimization Problems
Reliable classification of two-class cancer data
Chess game
Neural network topologies
A recommender system for music data
Music generation
Military fleet mix problems
Modelling land use change
Finding an optimal racing line in a vehicle simulator
Fuzzy system for autonomous mobile robot
League championship algorithm
Portfolio optimization problems
Optimization of waste collection in intelligent cities
Copyright © MM
Last updated 26.3.2024
