Course detail
Evolution Algorithms
FEKT-MEALAcad. year: 2018/2019
The course is focused on deterministic and stochastic optimization methods for finding global minima. It focuses on evolutionary algorithms with populations such as genetic algorithms, controlled random search, evolutionary strategies, particle swarm method, the method of ant colonies and more.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Implement a simple analytical optimization method (steepest descent and Newton's method)
To implement the simplex method for finding global extreme
Explain the nature of stochastic optimization methods with populations
Explain the nature of binary and continuous genetic algorithms and the basic operations
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
- 30 points can be obtained for activity in the laboratory exercises, consisting in solving tasks (for the procedure for the examination must be obtained at least 15 points)
- 70 points can be obtained for the written exam (the written examination is necessary to obtain at least 35 points)
Course curriculum
2. Method of steepest descent, Newton method
3. Simplex method, hill climbing, tabu search, simulated annealing (SA), control random search (CRS), evolution search (ES).
4. Differential evolution (DE), evolutionary strategy (ES)
5. Genetic algorithms (GA), binary GA
6. Continuous GA, Travel salesman problem (TSP) and GA
7. Genetic programming
8. Ant colony (AC), TSP and AC, TST and SA
9. Partical swarm optimization (PSO)
10. Algorithms inspired by fireflies, bats, cuckoos
11. Algorithms inspired by wolves and bees
12. MATLAB optimization, algorithms verification and comparison
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Basically:
- obligatory computer-lab tutorial (missed labs must be properly excused and can be replaced after agreement with the teacher)
- voluntary lecture.
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Method of steepest descent, Newton method
3. Simplex method, hill climbing, tabu search, simulated annealing (SA), control random search (CRS), evolution search (ES).
4. Differential evolution (DE), evolutionary strategy (ES)
5. Genetic algorithms (GA), binary GA
6. Continuous GA, Travel salesman problem (TSP) and GA
7. Genetic programming
8. Ant colony (AC), TSP and AC, TST and SA
9. Partical swarm optimization (PSO)
10. Algorithms inspired by fireflies, bats, cuckoos
11. Algorithms inspired by wolves and bees
12. MATLAB optimization, algorithms verification and comparison
Exercise in computer lab
Teacher / Lecturer
Syllabus
2. Method of steepest descent
3. Newton method
4. Simplex method
5. Binary GA 1 (1D)
6. Binary GA 2 (2D)
7. Continuous GA
8. TSP – introduction, SA
9. TSP – permutation GA
10. TSP – ACO
11. Fireflies
12. MATLAB optimization