Evolutionary Algorithms and Chaotic Systems 7088352102

3642107060
Studies in Computational Intelligence,Volume 267
Evolutionary Algorithms and
Chaotic Systems
Foreword
Preface
Acknowledgements
Contents
Part I: Theory
	Chapter 1
Motivation for Application of Evolutionary
Computation to Chaotic Systems
		Introduction
		Evolutionary Computation and Selected Examples
			Evolutionary Design
			Application of Evolvable Hardware
			Automatic Design of Low-Cost Hardware
			Poorly Specified Problems
			Adaptive Systems
			Fault Tolerant Systems
			Design Innovation in Poorly Understood Design Spaces
			Hummies Competition
			Problems Solvable by Evolutionary Computation
			Example: Real-Time Compensation of Plasma Reactor
		Chaotic Systems
		Conclusions
		References
	Chapter 2
Evolutionary Algorithms for Chaos Researchers
		Historical Facts from a Slightly Different Point of View
		Evolutionary Algorithms – Outline
			Central Dogma of Evolutionary Computational Techniques
			Evolutionary Algorithms and Importance of Their Use
		Selected Evolutionary Techniques
			Overview
			Current State
		Selected Basic Terms from the Evolutionary Algorithms
			The Usability Areas of Evolutionary Algorithms
			Common Features
			Population
			Individuals and Their Representation
			Evolutionary Operators: Selection, Recombination, Mutation
		Limits to Computation
			Searched Space and Its Complexity
			Physical Limits of Computation
		Conclusion
		References
	Chapter 3
Chaos Theory for Evolutionary Algorithms
Researchers
		Introduction
		Characterization of Deterministic Chaos
			Roots of Deterministic Chaos
		Universal Features of Chaos
			Determinism and Unpredictability of the Behavior of Deterministic Chaos – Sensitivity to Initial Conditions
			Lyapunov Exponents
			The U-Sequence
			Intermittence, Period Doubling, Metastable Chaos and Crises
			Feigenbaum Constants
			Self-similarity
		From Order to Chaos
			Period Doubling
			Intermittence
			Chaotic Transients
			Crises
		Selected Examples
			Mechanical System – Billiard
			Mechanical System – Duffing's Equation
			Electronic System – Chua's Circuit, Circuit with a Diode
			Biological System – Logistic Equation
			Meteorological System – Lorenz Weather Model
			Spatiotemporal Chaos
			Cellular Automata – Game of Life
			Artificial Intelligence – Neuron Networks
			Artificial Intelligence – Evolutionary Algorithms
			Astronomy – The Three-Body Problem
		Conclusion
		References
	Chapter 4
Evolutionary Algorithms and the Edge of Chaos
		Introduction
		Edge of Chaos
		Antichaos and Self-organization
			A Butterfly Sleeps
			Chaos and Antichaos
		Edge of Chaos in Evolutionary Algorithms
			Stagnation
			Anti-stagnation
		Analytical Observation
			Diversity Measure
			Population Representation
		Conclusion
		References
Part II: Applications
	Chapter 5
Evolutionary Design of Chaos Control in 1D
		Introduction
		Evolutionary Techniques in Chaos Control
		Chaotic Systems
			Logistic Equation
			Henon Map
		Selected Method for the Controlling of Chaos
			Delayed Feedback Control (Pyragas Method)
		Evolutionary Algorithms
		Optimization of Chaos Control
			Problem Design
			The Cost Function
			Experimental Results
			Analysis of All Results
		Comparison with OGY Method
			Logistic Equation
			Henon Map
		Conclusion and Discussion
		References
	Chapter 6
Evolutionary Control of CML Systems
		Introduction
		Motivation
		Selected Evolutionary Algorithm - A Brief Introduction
			Differential Evolution
			SOMA
			Simulated Annealing
			Genetic Algorithms
			Evolutionary Strategies
		CML Control
			Used Hardware
			Problem Selection and Case Studies
			Cost Function
			Parameter Setting
			Experimental Results
			CML Real Time Control
		Conclusion
		References
	Chapter 7
Chaotic Systems Reconstruction
		Introduction
		Unknown Inputs Multiple Observer Design
			Unknown Inputs Observer Design
			LMI Design Conditions
			Pole Placement
		Unknown Inputs Estimation
		Simulation Examples
			Academic Example
			Application to Chaotic System Reconstruction
		Extension to Discret-Time Multiple Model
			Pole Assignment
		Application to Chaotic System Reconstruction
		Conclusion
		References
	Chapter 8
Evolutionary Reconstruction of Chaotic Systems
		Introduction
		Motivation
		Chaos System Reconstruction – Classical Methods
			Reconstruction Based on Time Series Analysis
		Evolutionary Reconstruction of Chaotic Systems
			Problem Selection, Used Algorithms and Computer Technology
			The Cost Function
			Experiment Setup
			Experimental Results
			Reconstruction of Similar Systems
			Unfinished Evolution
			Exotic Solutions
			Continuous Systems: Preliminary Study
		Conclusion
		References
	Chapter 9
Cryptography Based on Spatiotemporal Chaotic
Systems
		Introduction
		CML-Based Pseudo-Random-Bit Generators
			Coupled Map Lattice
			Digitization Method
			Statistical Properties
			PRBGs Based on Various CMLs
		CML-Based Stream Cipher
			Algorithm of the Cipher
			Keyspace
			Cryptographic Properties of the Keystream
			High Efficiency
		CML-Based Multimedia Cryptosystem
			Design of CML-Based Multimedia Cryptosystem
			Performance Analysis
		Conclusion
		References
	Chapter 10
Evolutionary Decryption of Chaotically
Encrypted Information
		Introduction
		Motivation
		Selected Evolutionary Algorithm – A Brief Introduction
		Evolutionary Decryption
			Used Hardware, Problem Selection and Case Studies
			Cost Function
			Parameter Setting
			Experimental Results
		Conclusion
		References
	Chapter 11
Chaos Synthesis by Evolutionary Algorithms
		Introduction
		Motivation
		Brief Review of the Selected Evolutionary Algorithm
		Symbolic Regression – An Introduction
			Genetic Programming
			Grammatical Evolution
			Analytic Programming
		Experiment Design
			Parameter Setting
			Cost Function
			Case Studies
		Conclusion
		References
	Chapter 12
Evolutionary Synchronization of Chaotic
Systems
		Introduction
		Motivation
		Selected Evolutionary Algorithm – A Brief Introduction
		Evolutionary Synchronization
			Used Hardware, Problem Selection and Case Studies
			Cost Function
			Parameter Setting
			Experimental Results
		Conclusion
		References
	Chapter 13
Evolutionary Optimization and Dynamic Fitness
Landscapes
		Introduction
		Constructing Dynamic Fitness Landscapes from Reaction–Diffusion Systems and CML
			Static and Dynamic Fitness Landscapes
			Hierarchy of Fitness Landscapes
			Relationships between Coupled Map Lattices and Reaction–Diffusion Systems
		Properties of Dynamic Fitness Landscapes
			Topological Properties and Topological Problem Difficulty
			Dynamical Properties and Dynamical Problem Difficulty
			Topological and Dynamical Landscape Measures for the CML–Based Landscape
		Evolutionary Optimization
		Numerical Experiments
		Concluding Remarks
		References
	Chapter 14
Controller Parameters Optimization on a
Representative Set of Systems Using
Deterministic-Chaotic-Mutation Evolutionary
Algorithms
		Introduction
		PID Controller
			Proportional Algorithm
			Proportional Integral Algorithm
			Proportional Integral Derivative Algorithm
		Controller Tuning
			Ziegler Nichols Closed Loop Method
		System Specifications
			Sensitivity Specifications
			Optimization Specifications
		Differential Evolution Algorithm
			Tuning Parameters
		Chaotic Systems
			Lozi Map
			Delayed Logistic Map
		Problem Description
			Fourth Order System
			Third Order System
			Electric DC Motor
		Conclusion
		References
	Chapter 15
Chaotic Attributes and Permutative
Optimization
		Introduction
		Chaotic Signature in Population Dynamics
		Population Dynamics
			Initial Population
			Solution Dynamics
			Chaotic Features
			Selection and Deletion
			Dynamic Clustering
		Metaheuristics
			Genetic Algorithms
			Differential Evolution Algorithm
			Self Organizing Migrating Algorithm
		General Template
		Quadratic Assignment Problem
		Results
			Genetic Algorithm Results
			Differential Evolution Results
			Self Organizing Migration Algorithm Results
		Analysis
		Conclusion
		References
	Chapter 16
Frontiers
		References