
Evolutionary Algorithms in Theory and Practice
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Content
- Intro
- Contents
- Introduction
- I: A COMPARISON OF EVOLUTIONARY ALGORITHMS
- 1 Organic Evolution and Problem Solving
- 1.1 Biological Background
- 1.2 Evolutionary Algorithms and Artificial Intelligence
- 1.3 Evolutionary Algorithms and Global Optimization
- 1.4 Early Approaches
- 1.5 Summary
- 2 Specific Evolutionary Algorithms
- 2.1 Evolution Strategies
- 2.2 Evolutionary Programming
- 2.3 Genetic Algorithms
- 2.4 Summary
- 3 Artificial Landscapes
- 3.1 Sphere Model
- 3.2 Step Function
- 3.3 Ackley's Function
- 3.4 Function after Fletcher and Powell
- 3.5 Fractal Function
- 3.6 Summary
- 4 An Empirical Comparison
- 4.1 Convergence Velocity: ƒ[sub(1)], ƒ[sub(2)]
- 4.2 Convergence Reliability: ƒ[sub(3)], ƒ[sub(4)], and ƒ[sub(5)]
- 4.3 Summary
- II: EXTENDING GENETIC ALGORITHMS
- 5 Selection
- 5.1 Selection Mechanisms
- 5.2 Experimental Investigation of Selection
- 5.3 Summary
- 6 Mutation
- 6.1 Simplified Genetic Algorithms
- 6.2 Summary
- 7 An Experiment in Meta-Evolution
- 7.1 Parallel Evolutionary Algorithms
- 7.2 The Algorithm
- 7.3 Evolving Convergence Velocity
- 7.4 Summary
- Summary and Outlook
- APPENDICES
- A: Data for the Fletcher-Powell Function
- B: Data from Selection Experiments
- C: Software
- C.1 Overview
- C.2 Usage
- C.3 Data Collection
- D: The Multiprocessor Environment
- D.1 The Transputer System
- D.2 The Helios Operating System
- E: Mathematical Symbols
- Bibliography
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
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