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An Introduction to Genetic Algorithms - Melanie Mitchell

Contents



An Introduction to Genetic Algorithms
Mitchell Melanie
Chapter : Genetic Algorithms: An Overview
Overview
 A BRIEF HISTORY OF EVOLUTIONARY COMPUTATION
 THE APPEAL OF EVOLUTION
 BIOLOGICAL TERMINOLOGY
 SEARCH SPACES AND FITNESS LANDSCAPES
 ELEMENTS OF GENETIC ALGORITHMS
Examples of Fitness Functions

GA Operators
 A SIMPLE GENETIC ALGORITHM
 GENETIC ALGORITHMS AND TRADITIONAL SEARCH METHODS
 TWO BRIEF EXAMPLES
Using GAs to Evolve Strategies for the Prisoner's Dilemma
Hosts and Parasites: Using GAs to Evolve Sorting Networks
 HOW DO GENETIC ALGORITHMS WORK?
THOUGHT EXERCISES
COMPUTER EXERCISES
Chapter : Genetic Algorithms in Problem Solving
Overview
 EVOLVING COMPUTER PROGRAMS
Evolving Lisp Programs
Evolving Cellular Automata
 DATA ANALYSIS AND PREDICTION
Predicting Dynamical Systems
Predicting Protein Structure
 EVOLVING NEURAL NETWORKS
Evolving Weights in a Fixed Network
Evolving Network Architectures
Direct Encoding
Grammatical Encoding
Evolving a Learning Rule
THOUGHT EXERCISES
COMPUTER EXERCISES
Chapter : Genetic Algorithms in Scientific Models
Overview
 MODELING INTERACTIONS BETWEEN LEARNING AND EVOLUTION
The Baldwin Effect
A Simple Model of the Baldwin Effect
Evolutionary Reinforcement Learning
 MODELING SEXUAL SELECTION
Simulation and Elaboration of a Mathematical Model for Sexual Selection
 MODELING ECOSYSTEMS
 MEASURING EVOLUTIONARY ACTIVITY
Thought Exercises
Computer Exercises
Chapter : Theoretical Foundations of Genetic Algorithms
Overview
 SCHEMAS AND THE TWO−ARMED BANDIT PROBLEM
The Two−Armed Bandit Problem
Sketch of a Solution
Interpretation of the Solution
Implications for GA Performance
Deceiving a Genetic Algorithm
Limitations of "Static" Schema Analysis
 ROYAL ROADS
Royal Road Functions
Experimental Results
Steepest−ascent hill climbing (SAHC)
Next−ascent hill climbing (NAHC)
Random−mutation hill climbing (RMHC)
Analysis of Random−Mutation Hill Climbing
Hitchhiking in the Genetic Algorithm
An Idealized Genetic Algorithm
 EXACT MATHEMATICAL MODELS OF SIMPLE GENETIC ALGORITHMS
Formalization of GAs
Results of the Formalization
A Finite−Population Model
 STATISTICAL−MECHANICS APPROACHES
THOUGHT EXERCISES
COMPUTER EXERCISES
 WHEN SHOULD A GENETIC ALGORITHM BE USED?
 ENCODING A PROBLEM FOR A GENETIC ALGORITHM
Binary Encodings
Many−Character and Real−Valued Encodings
Tree Encodings
 ADAPTING THE ENCODING
Inversion
Evolving Crossover "Hot Spots"
Messy Gas
 SELECTION METHODS
Fitness−Proportionate Selection with "Roulette Wheel" and "Stochastic Universal"
Sampling
Sigma Scaling
Elitism
Boltzmann Selection
Rank Selection
Tournament Selection
Steady−State Selection
 GENETIC OPERATORS
Crossover
Mutation
Other Operators and Mating Strategies
 PARAMETERS FOR GENETIC ALGORITHMS
THOUGHT EXERCISES
COMPUTER EXERCISES
Chapter : Conclusions and Future Directions
Overview
Incorporating Ecological Interactions
Incorporating New Ideas from Genetics
Incorporating Development and Learning
Adapting Encodings and Using Encodings That Permit Hierarchy and Open−Endedness
Adapting Parameters
Connections with the Mathematical Genetics Literature
Extension of Statistical Mechanics Approaches
Identifying and Overcoming Impediments to the Success of GAs
Understanding the Role of Schemas in GAs
Understanding the Role of Crossover
Theory of GAs With Endogenous Fitness
Appendix A: Selected General References
Appendix B: Other Resources
SELECTED JOURNALS PUBLISHING WORK ON GENETIC ALGORITHMS
SELECTED ANNUAL OR BIANNUAL CONFERENCES INCLUDING WORK ON
GENETIC ALGORITHMS
INTERNET MAILING LISTS, WORLD WIDE WEB SITES, AND NEWS GROUPS WITH
INFORMATION AND DISCUSSIONS ON GENETIC ALGORITHMS
Bibliography

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