Evolutionary Computations New Algorithms and their Applications to Evolutionary Robots /
Evolutionary Computation, a broad field that includes Genetic Algorithms, Evolution Strategies, and Evolutionary Programming, has proven to offer well-suited techniques for industrial and management tasks - therefore receiving considerable attention fom scientists and engineers during the last decad...
| Κύριοι συγγραφείς: | , |
|---|---|
| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2004.
|
| Έκδοση: | 1st ed. 2004. |
| Σειρά: | Studies in Fuzziness and Soft Computing,
147 |
| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- 1. Evolutionary Algorithms: Revisited
- 1.1 Introduction
- 1.2 Stochastic Optimization Algorithms
- 1.3 Properties of Stochastic Optimization Algorithms
- 1.4 Variants of Evolutionary Algorithms
- 1.5 Basic Mechanisms of Evolutionary Algorithms
- 1.6 Similarities and Differences of Evolutionary Algorithms
- 1.7 Merits and Demerits of Evolutionary Algorithms
- 1.8 Summary
- 2. A Novel Evolution Strategy Algorithm
- 2.1 Introduction
- 2.2 Development of New Variation Operators
- 2.3 Proposed Novel Evolution Strategy
- 2.4 Proposed NES: How Does It Work?
- 2.5 Performance of the Proposed Evolution Strategy
- 2.6 Empirical Investigations for Exogenous Parameters
- 2.7 Summary
- 3. Evolutionary Optimization of Constrained Problems
- 3.1 Introduction
- 3.2 Constrained Optimization Problem
- 3.3 Constraint-Handling in Evolutionary Algorithms
- 3.4 Characteristics of the NES Algorithm
- 3.5 Construction of the Constrained Fitness Function
- 3.6 Test Problems
- 3.7 Implementation, Results and Discussions
- 3.8 Summary
- 4. An Incest Prevented Evolution Strategy Algorithm
- 4.1 Introduction
- 4.2 Incest Prevention: A Natural Phenomena
- 4.3 Proposed Incest Prevented Evolution Strategy
- 4.4 Performance of the Proposed Incest Prevented Evolution Strategy
- 4.5 Implementation and Experimental Results
- 4.6 Summary
- 5. Evolutionary Solution of Optimal Control Problems
- 5.1 Introduction
- 5.2 Conventional Variation Operators
- 5.3 Optimal Control Problems
- 5.4 Simulation Examples
- 5.5 Results and Discussions
- 5.6 Summary
- 6. Evolutionary Design of Robot Controllers
- 6.1 Introduction
- 6.2 A Mobile Robot with Two Independent Driving Wheels
- 6.3 Optimal Servocontroller Design for the Robot
- 6.4 Construction of the Fitness Function for the Controllers
- 6.5 Considerations for Design and Simulations
- 6.6 Results and Discussions
- 6.7 Summary
- 7. Evolutionary Behavior-Based Control of Mobile Robots
- 7.1 Introduction
- 7.2 An Evolution Strategy Using Statistical Information of Subgroups
- 7.3 Omnidirectional Mobile Robot
- 7.4 Fuzzy Behavior-Based Control System
- 7.5 Acquisition of Control System
- 7.6 Summary
- 8. Evolutionary Trajectory Planning of Autonomous Robots
- 8.1 Introduction
- 8.2 Fundamentals of Evolutionary Trajectory Planning
- 8.3 Formulation of the Problem for Trajectory Planning
- 8.4 Polygonal Obstacle Sensing and Its Representation
- 8.5 Special Representations of Evolutionary Components
- 8.6 Construction of the Fitness Function
- 8.7 Bounds for Evolutionary Parameters
- 8.8 Proposed Evolutionary Trajectory Planning Algorithm
- 8.9 Considerations and Simulations
- 8.10 Results and Discussions
- 8.11 Summary
- A. Definitions from Probability Theory and Statistics
- A.1 Random Variables, Distributions and Density Functions
- A.2 Characteristics Values of Probability Distributions
- A.2.1 One Dimensional Distributions:
- A.2.2 Multidimensional Distributions
- A.3 Special Distributions
- A.3.1 The Normal or Gaussian Distribution
- A.3.4 The Cauchy Distribution
- B. C-Language Source Code of the NES Algorithm
- C. Convergence Behavior of Evolution Strategies
- C.1 Convergence Reliability
- C.2 Convergence Velocity
- References.
