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Lista de Disciplinas | CMP166

Evolutionary Dynamical Systems

Professor: Dante Barone
Prerequisites: –
Hours: 60 hs
Credits: 4
Semesters: First semester
Undergraduate Enrollment: The enrollment must be made as Special Student
Page Link: –


Linear and Nonlinear Stability.
Deterministic Chaos and Routes to Chaos.
Characterization of Deterministic Chaos & Fractals.
Stochastic algorithms.
Genetic Algorithms.
Evolutionary Programming.
Swarm Intelligence.


The course analyzes how phenomena and paradigms that model the real world can be used by computer science to solve a wide spectrum of problems such as optimization, control, prediction and time series analysis. We will study both the stochastic nature of phenomena as the nature of deterministic chaos.

As for the techniques that make use of randomness in the search for solutions, we will analyze the Simulated Annealing, Hill Climbing, Genetic Algorithms, Genetic Programming and other bio-inspired techniques. As for the study of deterministic characteristics of phenomena apparently stochastic, we will analyze fractals, behavior of dynamic systems and other related topics. The students should develop practical implementations of the concepts and paradigms seen during the course.


Linear and Nonlinear Stability
Linear stability and classification of equilibrium points
Non-linear systems, linearization, nonlinear stability and bifurcations
Fixed points, linear stability and bifurcations in maps
Strange attractors
Lotka-Volterra model, Logistic Map and some other maps
Deterministic Chaos and Routes to Chaos
Deterministic Chaos
Lyapunov characteristic exponents
Kolmogorov-Sinai entropy
Experimental evidence of chaos in different applications
Characterization of Deterministic Chaos
Calculation of Lyapunov exponents
Fractal dimension, generalized dimensions and spectrum of singularities
Stochastic Algorithms
Characteristics of Stochastic Systems
Hill Climbing
Simulated Annealing
Genetic Algorithms
Genetic Operators.
Adjustment function.
Practical Applications.
Schema theorem.
Evolutionary Programming
Machine Learning.
Evolutionary programs and heuristics.
Time series forecasting
Swarm Intelligence.
Particle Swarm Optimization.
Ant Colony Intelligence.
Artificial Immune Systems.


Eiben, A. E.; Smith, J. E. Introduction to Evolutionary Computing, Springer, First edition, 2003, ISBN 3-540-40184-9, Corrected 2nd printing, 2007, ISBN: 978-3-540-40184-1.
De Jong, K. A. Evolutionary computation: a unified approach, MIT Press, 2006.
Banzhaf, W. Genetic Programming: An Introduction on the Automatic Evolution of Computer Programs and Its Applications. Morgan Kaufmann Publishers, 1997.
De Castro, L. N.; Timmis, J. Artificial Immune Systems: A New Computational Intelligence Approach. Springer, 2002
Luke, S. Essentials of Metaheuristics. Lulu Press, 2011.
Poli, R.; Langdon, W.; McPhee, N. A Field Guide to Genetic Programming. Lulu Press, 2008.

FLEDLER-FERRARA, Nelson & CINTRA THE PRADO, Carmen – CHAOS – An Introduction, Editora Edgard Blücher, 1994.
Cambel, AB – Applied Chaos Theory – Paradigm for Complexity, Academic Press, 1993.
Gleick, James – Chaos – The Creation of a New Science – Free Press, 1989.
PRITCHARD, Joe – The Chaos Cookbook – a practical programming, 2nd edition, Butterworth – Heinemann, 1996.
PEITGEN, Heinz-Otto Juergens, Hartmut & Saupe, Dietmar – Fractals for the Classroom, Springer-Verlag, 1992.
ALLIOT, JM et allii – Artificial Evolution – Lecture Notes in Computer Science – 1063, Springer-Verlag, 1996.
Levy, Steven – Artificial Life – A Report from the Frontier Where Computers Meet Biology, Vintage Books, 1992.
MICHALEWZKI, Zbigniew – Genetic Algoritms + Data Structures = Evolution Programs – Springer, 1992.
Mitchell, Melanie – An Introduction to Genetic Algorithms – MIT Press, 1996.
DAVIS, Lawrence – Handbook of Genetic Algorithms, Van Nostrand Reinhold, 1991.
RUCKER, Rudy – Artificial Life Lab, Waite Group Press, 1993.