### Course Description

# Evolutionary Algorithms

## Program

Information and Communication Technologies, third-level study programme## Lecturers:

prof. dr. Bogdan Filipič## Goals:

The course objectives are to (a) introduce the basics of optimization and evolutionary computation, (b) present the building-blocks and mechanisms of evolutionary algorithms and their characteristics, (c) present the methodology of result evaluation and the algorithm practical potential, (d) give an overview of the related algorithms.

The students who will successfully complete this course will master the basics of evolutionary computation and will be capable of applying evolutionary algorithms in solving demanding optimization problems and evaluating their results.

## Content:

Introduction:

search and optimization, optimization problems and their characteristics, deterministic and stochastic optimization, nature-inspired optimization algorithms, evolutionary computation, computational intelligence

Foundations of evolutionary algorithms:

motivation, terminology, composition and functioning, types of evolutionary algorithms, theoretical background, advantages and disadvantages

Mechanisms and techniques:

algorithm parameter tuning, constraint handling, solving multimodal, dynamic and multiobjective optimization problems, parallelization, hybridization

Evaluation and applications:

statistical analysis of results, measures of effectiveness and efficiency, design of an evolutionary algorithm for a selected optimization problem, use cases from science, engineering and business

Related algorithms:

particle swarm optimization, ant colony optimization, cultural algorithms, memetic algorithms, artificial immune systems

## Course literature:

Selected chapters from the following books:

• Th. Bäck, Ch. Foussette, and P. Krause. Contemporary Evolution Strategies. Springer, 2013. ISBN 978-3-642-40136-7

• A. E. Eiben, and J. E. Smith. Introduction to Evolutionary Computing, 2nd edition. Springer, 2015. ISBN 978-3-662-44873-1

• Th. Jansen. Analyzing Evolutionary Algorithms. Springer, 2013. ISBN 978-3-642-17338-7

• G. Rozenberg, Th. Bäck, and J. N. Kok (Eds.). Handbook of Natural Computing. Springer, 2012. ISBN 978-3-540-92909-3

• X. Yu, and M. Gen. Introduction to Evolutionary Algorithms. Springer, 2010. ISBN 978-1-84996-128-8

## Significant publications and references:

• T. Tušar, and B. Filipič. “Visualization of Pareto front approximations in evolutionary multiobjective optimization: A critical review and the prosection method.” IEEE Transactions on Evolutionary Computation, vol. 19, no. 2, pp. 225-245, 2015.

• M. Mlakar, D. Petelin, T. Tušar, and B. Filipič. “GP-DEMO: Differential evolution for multiobjective optimization based on Gaussian process models.” European Journal of Operational Research, vol. 243, no. 2, pp. 347-361, 2015.

• E. Dovgan, M. Javorski, T. Tušar, M. Gams, and B. Filipič. “Discovering driving strategies with a multiobjective optimization algorithm.” Applied Soft Computing, vol. 16, no. 1, pp. 50-62, 2014.

• M. Depolli, R. Trobec, and B. Filipič. “Asynchronous master-slave parallelization of differential evolution for multiobjective optimization.” Evolutionary Computation, vol. 21, no. 2, pp. 261-291, 2013.

• P. Korošec, J. Šilc, and B. Filipič. “The differential ant-stigmergy algorithm.” Information Sciences, vol. 192, no. 1, pp. 82-97, 2012.

## Examination:

Written or oral exam (100%)

## Students obligations:

Coursework, written or oral exam