Lectures:
1.Evolutionary algorithms 1. The current state of the field softcomputing, fuzzy logic, neural
networks, evolutionary computing (EVT). Classification of evolutionary computation,
historical facts, current trends in EVT. The central dogma, according to Darwin, and EVT
Mendel.
2. Evolutionary Algorithms 2. No Free Lunch Theorem. Computational complexity and physical limitations
algorithms. Multipurpose optimization and Pareto set.
3. Evolutionary algorithms 3. Restrictions placed on the utility function and individual parameters.
Penalties and its impact on the geometry of the objective function. Working with real, integer and
discrete values of individual parameters. Genetic algorithms. GA terminology. Principle
activities, Hybrid GA, messy GA, parallel GA, migration and diffusion models.
4. Evolutionary algorithms 4. Evolutionary Strategy. No-man (1 +1)-EC.Multi-EC (μ
+ Λ)-ES and (μ, λ)-ES. Multi-EC (μ + λ)-ES and (μ, λ)-ES. Adaptive EC.Particle swarm
(Particle Swarm). Search suspended (Scatter Search). Ant colony optimization (ant
Colony Optimization).
5. Evoluční algorithms 5. SOMA: Self-Organizing Migrating Algorithm principle of operation
and strategies used by the algorithm: ATO, ATR ATAA and ATA. Differential evolution principle
activities and used versions: DE/best/1/exp, DE/rand/1/exp, DE/rand-to-best/1/exp, DE/best/2 /
exp DE/rand/2/exp, DE/best/1/bin, DE/rand/1/bin, DE/rand-to-best/1/bin, DE/best/2/bin,
DE/rand/2/bin. SOMA, DE and permutation test problems.
6. Evolutionary algorithms 6. Techniques of Genetic Programming: Genetic Programming,
grammatical evolution. Alternatives: analytical programming, Probabilistic Incremental
Program Evolution - PIPE, Gene Expression Programming, Programming Multiexpression
and more.
7. Evolutionary Hardware (EH). Inspiration in biology. Computing devices.Reconfigurable
equipment. Evolutionary design and digital circuits. EH and cellular automata. Polymorphous
electronics.
8. Cellular Automata (BA) and complex systems. Introduction, Formalism BA
Dynamics and classification according to Wolfram's cellular automata, Conway's Game of Life,
using BA modeling.
9. Artificial life. Basic definitions and existing systems and models. Tierra, biomorf, Sbeat,
Sbart, Eden, Galapagos ... Self-reproducing automata according to Turing and von
Neumann. Langton's loop, computer viruses and artificial life. Artificial Life and edge
chaos (according to Kaufmann)
10. Neural Networks (ANN). History and basic principles of NS. The training set and its use
NS. The basic types of networks and their applications to different types of problems.
11. Fractal geometry. History, definition of fractal, basic types of algorithms that generate
fractals. Fractal dimension, interpolation and compression. Developmental systems and artificial life. L systems,
turtle graphics, parametric L-systems, L-systems from the perspective of fractal geometry.
12. Immunological systems (IS). The principle of the IS, the IS limits, algorithms implementing IS imunotronics.
13. Swarm Intelligence (SI). Basic concepts and definitions, representative algorithms SI -
Particle Swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution
complex systems.
14. DNA computing. DNA computing as part of bioinformatics, DNA and binary
representation according to Adlemann. Watson Crickův machine. Mathematical modeling operations on DNA.
Laboratories (for PC classrooms):
The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples.
- Creation of a single basic framework for bio-inspired algorithms on the principles of GUI, 1 week
- Creation of a module for generating population and fitness function, 1 week
- Creation of a module selection techniques for parents (suitable candidates) to create offspring (better solution), 1 week
- Creation of a module for crossover, 1 week
- Creation of a modules of evolutionary algorithms, 4 weeks
- Creation of a modules of symbolic regression, 4 weeks
- Creation of a module with a simple cellular automaton, 1 week
1.Evolutionary algorithms 1. The current state of the field softcomputing, fuzzy logic, neural
networks, evolutionary computing (EVT). Classification of evolutionary computation,
historical facts, current trends in EVT. The central dogma, according to Darwin, and EVT
Mendel.
2. Evolutionary Algorithms 2. No Free Lunch Theorem. Computational complexity and physical limitations
algorithms. Multipurpose optimization and Pareto set.
3. Evolutionary algorithms 3. Restrictions placed on the utility function and individual parameters.
Penalties and its impact on the geometry of the objective function. Working with real, integer and
discrete values of individual parameters. Genetic algorithms. GA terminology. Principle
activities, Hybrid GA, messy GA, parallel GA, migration and diffusion models.
4. Evolutionary algorithms 4. Evolutionary Strategy. No-man (1 +1)-EC.Multi-EC (μ
+ Λ)-ES and (μ, λ)-ES. Multi-EC (μ + λ)-ES and (μ, λ)-ES. Adaptive EC.Particle swarm
(Particle Swarm). Search suspended (Scatter Search). Ant colony optimization (ant
Colony Optimization).
5. Evoluční algorithms 5. SOMA: Self-Organizing Migrating Algorithm principle of operation
and strategies used by the algorithm: ATO, ATR ATAA and ATA. Differential evolution principle
activities and used versions: DE/best/1/exp, DE/rand/1/exp, DE/rand-to-best/1/exp, DE/best/2 /
exp DE/rand/2/exp, DE/best/1/bin, DE/rand/1/bin, DE/rand-to-best/1/bin, DE/best/2/bin,
DE/rand/2/bin. SOMA, DE and permutation test problems.
6. Evolutionary algorithms 6. Techniques of Genetic Programming: Genetic Programming,
grammatical evolution. Alternatives: analytical programming, Probabilistic Incremental
Program Evolution - PIPE, Gene Expression Programming, Programming Multiexpression
and more.
7. Evolutionary Hardware (EH). Inspiration in biology. Computing devices.Reconfigurable
equipment. Evolutionary design and digital circuits. EH and cellular automata. Polymorphous
electronics.
8. Cellular Automata (BA) and complex systems. Introduction, Formalism BA
Dynamics and classification according to Wolfram's cellular automata, Conway's Game of Life,
using BA modeling.
9. Artificial life. Basic definitions and existing systems and models. Tierra, biomorf, Sbeat,
Sbart, Eden, Galapagos ... Self-reproducing automata according to Turing and von
Neumann. Langton's loop, computer viruses and artificial life. Artificial Life and edge
chaos (according to Kaufmann)
10. Neural Networks (ANN). History and basic principles of NS. The training set and its use
NS. The basic types of networks and their applications to different types of problems.
11. Fractal geometry. History, definition of fractal, basic types of algorithms that generate
fractals. Fractal dimension, interpolation and compression. Developmental systems and artificial life. L systems,
turtle graphics, parametric L-systems, L-systems from the perspective of fractal geometry.
12. Immunological systems (IS). The principle of the IS, the IS limits, algorithms implementing IS imunotronics.
13. Swarm Intelligence (SI). Basic concepts and definitions, representative algorithms SI -
Particle Swarm, scatter search, ant colony optimization, swarm robotic, artificial evolution
complex systems.
14. DNA computing. DNA computing as part of bioinformatics, DNA and binary
representation according to Adlemann. Watson Crickův machine. Mathematical modeling operations on DNA.
Laboratories (for PC classrooms):
The seminar will focus on the practical application of the discussed techniques and solutions of selected problem examples.
- Creation of a single basic framework for bio-inspired algorithms on the principles of GUI, 1 week
- Creation of a module for generating population and fitness function, 1 week
- Creation of a module selection techniques for parents (suitable candidates) to create offspring (better solution), 1 week
- Creation of a module for crossover, 1 week
- Creation of a modules of evolutionary algorithms, 4 weeks
- Creation of a modules of symbolic regression, 4 weeks
- Creation of a module with a simple cellular automaton, 1 week