An introduction to differential evolution
New ideas in optimization
An Evolutionary Algorithm for Controlling Chaos: The Use of Multi-objective Fitness Functions
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On Step Width Adaptation in Simulated Annealing for Continuous Parameter Optimisation
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Handbook of Chaos Control
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Brief paper: A swarm intelligence approach to the synthesis of two-dimensional IIR filters
Engineering Applications of Artificial Intelligence
Design of fuzzy power system stabilizer using adaptive evolutionary algorithm
Engineering Applications of Artificial Intelligence
Visualization of complex networks dynamics: case study
IFIP'12 Proceedings of the 2012 international conference on Networking
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This contribution introduces continuation of an investigation on deterministic spatiotemporal chaos real-time control by means of selected evolutionary techniques. Real-time-like behavior is specially defined and simulated with the spatiotemporal chaos model based on mutually nonlinearly joined n equations, so-called coupled map lattices (CML). Four evolutionary algorithms are used for chaos control here: differential evolution, self-organizing migrating algorithm, genetic algorithm and simulated annealing in a total of 12 versions. For modeling of real-time spatiotemporal chaos behavior, the so-called CML were used based on logistic equation to generate chaos. The main aim of this investigation was to show that evolutionary algorithms, under certain conditions, are capable of real-time control of deterministic chaos, when the cost function is properly defined as well as parameters of selected evolutionary algorithm. Investigation consists of four different case studies with increasing simulation complexity. For all algorithms, each simulation was repeated 100 times to show and check robustness of used methods. All data were processed and used in order to get summarizing results and graphs.