Bio Inspired Algorithms
Code  Completion  Credits  Range  Language 

AD4M33BIA  Z,ZK  6  14KP+6KC  Czech 
 Garant předmětu:
 Lecturer:
 Tutor:
 Supervisor:
 Department of Cybernetics
 Synopsis:

The students will learn some of the uncoventional methods of computational intelligence aimed at solving complex tasks of classification, modeling, clustering, search and optimization. Bioinspired algorithms take advantage of analogies to various phenomena in the nature and society. The main topics of the subject are artificial neural networks and evolutionary algorithms.
 Requirements:

Basic knowledge of optimization
 Syllabus of lectures:

1.Introduction  relations to conventional optimization methods, blackbox optimization, randomized search methods.
2.Introduction to artificial neural networks, history, typical tasks and their solutions, types of neural networks learning. Perceptron.
3.Supervised learning  approximation and classification, local and global units in neural networks. Multilayered perceptron, RBF networks, GMDH networks.
4.Unsupervised learning  clustering with neural networks, selforganization, Hebb's rule, Hopfield network, associative memory, ART networks.
5.Kohonen's selforganizing map (SOM), competitive learning, reinforcement learning.
6.Error backpropagation algorithm, universal approximation, Kolmogorov theorem.
7.Temporal sequences processing, recurrent neural networks, Elman network, backpropagation through time.
8.Simple genetic algorithm (SGA)  history, basic cycle, genetic operators, schema theorem.
9.Evolutionary algorithms with real representation  evolutionary strategy, crossover operators. Differential evolution (DE).
10.Neuroevolution  evolutionary techniques for neural network structure learning and parameter tunning. NEAT system.
11.Multiobjective optimization  dominance principle, Paretooptimal solutions, multiobjective evolutionary algorithms (NSGAII, SPEA2).
12.Genetic programming (GP)  tree representation, initialization, operators, stronglytyped GP, automatically defined functions (ADF).
13.Reserved.
 Syllabus of tutorials:

1.Seminar organization. Black box neural network (MLP), approximation, classification, local search examples in Matlab.
2.Neural network software, Mathematica, Weka.
3.First assignment introduction (introduction to data).
4.Elaboration of the first assignment.
5.Elaboration of the first assignment.
6.First assignment presentation and evaluation.
7.Second assignment introduction (evolutionary algorithms).
8.Simple genetic algorithm (SGA). Influence of SGA parameters on its behaviour. Examples of evolutionary algorithms in Matlab.
9.Elaboration of the second assignment.
10.Successful applications of evolutionary algorithms.
11.Test.
12.Second assignment handin and evaluation.
13.Assignments.
 Study Objective:

The goal of this subject is to acquaint students with unconventional methods of computational intelligence aimed at solving complex tasks of classification, modeling, clustering, search and optimization. The main topics of the subject are artificial neural networks and evolutionary algorithms.
 Study materials:

1.Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edition, Prentice Hall, 1998
2.Rojas, R.: Neural Networks: A Systematic Introduction, Springer, 1996
3.Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1998
4.Michalewicz, Z.: How to solve it? Modern heuristics. 2nd ed. Springer, 2004.
 Note:
 Further information:
 http://cw.felk.cvut.cz/doku.php/courses/ad4m33bia/start
 No timetable has been prepared for this course
 The course is a part of the following study plans: