On the inference of deterministic chaos: Evolutionary algorithm and metabolic P system approaches

In this chapter, we do synthesis of three partially different areas of research: complex networks, evolutionary computation and deterministic chaos. Ideas, results and methodologies reported and mentioned here are based on our previous results and experiments. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). In order to understand what is the main idea, lets first discuss an overview of the two main areas: complex networks and evolutionary algorithms.

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On Mutual Relations amongst Evolutionary Algorithm Dynamics and Its Hidden Complex Network Structures

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch011 ◽

2016 ◽

pp. 215-239 ◽

Cited By ~ 1

Author(s):

Ivan Zelinka

Keyword(s):

Evolutionary Algorithms ◽

Complex Networks ◽

Evolutionary Algorithm ◽

Evolutionary Computation ◽

Complex Network ◽

Deterministic Chaos ◽

Main Idea ◽

Network Structures ◽

Research Complex ◽

Mutual Relations

In this chapter, we do synthesis of three partially different areas of research: complex networks, evolutionary computation and deterministic chaos. Ideas, results and methodologies reported and mentioned here are based on our previous results and experiments. We report here our latest results as well as propositions on further research that is in process in our group (http://navy.cs.vsb.cz/). In order to understand what is the main idea, lets first discuss an overview of the two main areas: complex networks and evolutionary algorithms.

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GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering

Computational Intelligence and Neuroscience ◽

10.1155/2017/4367342 ◽

2017 ◽

Vol 2017 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Yanhua Wang ◽

Xiyu Liu ◽

Laisheng Xiang

Keyword(s):

Genetic Algorithm ◽

Evolutionary Algorithm ◽

Clustering Algorithm ◽

Search Algorithm ◽

Membrane System ◽

P System ◽

Data Sets ◽

Ensemble Clustering ◽

Improved Genetic Algorithm ◽

Robust Clustering

Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

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An Improved Quantum-Inspired Evolutionary Algorithm Based on P Systems with a Dynamic Membrane Structure for Knapsack Problems

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1528 ◽

2012 ◽

Vol 239-240 ◽

pp. 1528-1531 ◽

Cited By ~ 1

Author(s):

Xue Bai Zhang ◽

Ge Xiang Zhang ◽

Ji Xiang Cheng

Keyword(s):

Evolutionary Algorithms ◽

Evolutionary Algorithm ◽

Membrane Structure ◽

Population Diversity ◽

P Systems ◽

Knapsack Problems ◽

P System ◽

Dynamic Adjustment ◽

Dynamic Membrane ◽

Elementary Membrane

To improve the performance of Quantum-inspired Evolutionary algorithm based on P Systems (QEPS), this paper presents an improved QEPS with a Dynamic Membrane Structure (QEPS-DMS) to solve knapsack problems. QEPS-DMS combines quantum-inspired evolutionary algorithms (QIEAs) with a P system with a dynamic membrane structure. In QEPS-DMS, a QIEA is considered as a subalgorithm to put inside each elementary membrane of a one-level membrane structure, which is dynamically adjusted in the process of evolution by applying a criterion for measuring population diversity. The dynamic adjustment includes the processes of membrane dissolution and creation. Knapsack problems are applied to test the effectiveness of QEPS-DMS. Experimental results show that QEPS-DMS outperforms QEPS and three variants of QIEAs recently reported in the literature.

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Structural aspects of silicon diffusion in quaternary III-V thin-film semiconductors

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100088567 ◽

1991 ◽

Vol 49 ◽

pp. 850-851

Author(s):

F. A. Ponce ◽

R. L. Thornton ◽

G. B. Anderson

Keyword(s):

Semiconductor Lasers ◽

Wide Bandgap ◽

Semiconductor Diode ◽

Visible Range ◽

P System ◽

Misfit Dislocations ◽

Lattice Match ◽

Semiconductor Diode Laser ◽

Thin Film Semiconductors ◽

Silicon Diffusion

The InGaAlP quaternary system allows the production of semiconductor lasers emitting light in the visible range of the spectrum. Recent advances in the visible semiconductor diode laser art have established the viability of diode structures with emission wavelengths comparable to the He-Ne gas laser. There has been much interest in the growth of wide bandgap quaternary thin films on GaAs, a substrate most commonly used in optoelectronic applications. There is particular interest in compositions which are lattice matched to GaAs, thus avoiding misfit dislocations which can be detrimental to the lifetime of these materials. As observed in Figure 1, the (AlxGa1-x)0.5In0.5P system has a very close lattice match to GaAs and is favored for these applications.In this work, we have studied the effect of silicon diffusion in GaAs/InGaAlP structures. Silicon diffusion in III-V semiconductor alloys has been found to have an disordering effect which is associated with removal of fine structures introduced during growth. Due to the variety of species available for interdiffusion, the disordering effect of silicon can have severe consequences on the lattice match at GaAs/InGaAlP interfaces.

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Complexity Measures of Brain Electrophysiological Activity

Journal of Psychophysiology ◽

10.1027/0269-8803/a000024 ◽

2010 ◽

Vol 24 (2) ◽

pp. 131-135 ◽

Cited By ~ 5

Author(s):

Włodzimierz Klonowski ◽

Pawel Stepien ◽

Robert Stepien

Keyword(s):

Brain Activity ◽

Deterministic Chaos ◽

Epileptic Seizures ◽

Eeg Signal ◽

Eeg Signals ◽

Complexity Measures ◽

Electrophysiological Activity ◽

Signal Complexity ◽

Physiological Sleep ◽

The Brain

Over 20 years ago, Watt and Hameroff (1987 ) suggested that consciousness may be described as a manifestation of deterministic chaos in the brain/mind. To analyze EEG-signal complexity, we used Higuchi’s fractal dimension in time domain and symbolic analysis methods. Our results of analysis of EEG-signals under anesthesia, during physiological sleep, and during epileptic seizures lead to a conclusion similar to that of Watt and Hameroff: Brain activity, measured by complexity of the EEG-signal, diminishes (becomes less chaotic) when consciousness is being “switched off”. So, consciousness may be described as a manifestation of deterministic chaos in the brain/mind.

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