Course detail

Evolution Algorithms

FEKT-FEALAcad. 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

Czech

Number of ECTS credits

5

Mode of study

Not applicable.

Learning outcomes of the course unit

The graduate of the course is capable of:
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

The knowledge on the Bachelor´s degree level is requested, namely on numerical mathematics. The laboratory work is expected knowledge of Matlab programming environment.

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching methods include lectures and computer laboratories. Course is taking advantage of e-learning system. Students have to write a single project/assignment during the course.

Assesment methods and criteria linked to learning outcomes

Requirements for completion of a course are specified by a regulation issued by the lecturer responsible for the course and updated for every year.
- 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

1. Introduction to mathematical optimization, gradient, hessian.
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

Not applicable.

Aims

Obtaining an understanding about deterministic and stochastic optimization methods. Introduction to the evolutionary algorithms with populations for finding the global extremes multidimensional functions. Introduction to the genetic programming.

Specification of controlled education, way of implementation and compensation for absences

Delimitation of controlled teaching and its procedures are specified by a regulation issued by the lecturer responsible for the course and updated for every year (see Rozvrhové jednotky).
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

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Tvrdík, J.: Evoluční algoritmy. Skripta, Přírodovědecká fakulta Ostravské univerzity, 2004 (CS)

Recommended reading

Hynek, J.: Genetické algoritmy a genetické programování. Grada Publishing, 2008 (CS)

Classification of course in study plans

  • Programme BTBIO-F Master's

    branch F-BTB , 2. year of study, winter semester, compulsory

  • Programme EEKR-CZV lifelong learning

    branch ET-CZV , 1. year of study, winter semester, compulsory

Type of course unit

 

Lecture

26 hours, optionally

Teacher / Lecturer

Syllabus

1. Introduction to mathematical optimization, gradient, hessian.
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

26 hours, compulsory

Teacher / Lecturer

Syllabus

1. Introduction to mathematical optimization
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
13. Student project