Table of Contents
Overcoming Fitness Barriers in Multi-Modal Search Spaces
2 PRELIMINARY OBSERVATION OF CYCLIC PHASE BEHAVIOUR IN PERFORMANCE PROFILES
3 THE H-IFF PERFORMANCE PROFILE
5 ‘BEST FOUND’ FITNESS DISTRIBUTIONS
6 FURTHER EXPERIMENTS: KAUFFMAN NK, ROYAL STAIRCASE AND MAX-ONES
New Methods for Tunable, Random Landscapes
5 More on Juxtapositional Complexity
8 Observations About the New Generator
Analysis of recombinative algorithms on a non-separable building-block problem
2 ANALYSIS ON SEPARABLE PROBLEMS
Direct Statistical Estimation of GA Landscape Properties
3 FIRST REPETITION WAITING TIME DISTRIBUTION
3.1 Maximum likelihood estimation
4 ESTIMATING THE NUMBER OF SAMPLES
6 CONCLUSIONS AND FURTHER WORK
Comparing population mean curves
5 The velocity as a hardness measure
6 Conclusions and further work
Local Performance of the (μ/μI, λ)-ES in a Noisy Environment
2 ALGORITHM AND FITNESS ENVIRONMENT
APPENDIX B: COMPUTING THE MEAN OF 〈z1〉
Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms
2 SOME ASSUMPTIONS AND DEFINITIONS
3 PROBABILISTIC SCHEMA THEOREMS WITHOUT EXPECTED VALUES
5 A POSSIBLE ROUTE TO PROVING GA CONVERGENCE
6 RECURSIVE CONDITIONAL SCHEMA THEOREM
7 CONDITIONAL CONVERGENCE PROBABILITY
Towards a Theory of Strong Overgeneral Classifiers
4 WHEN ARE STRONG OVERGENERALS POSSIBLE?
7 THE SURVIVAL OF RULES UNDER THE GA
Evolutionary Optimization Through PAC Learning
4 Why We Should Work in a PAC Setting
5 PAC Learning Applied to Evolutionary Optimization
6 Transformation of a Representation
8 Finding High Correlation Parity Strings
Continuous Dynamical System Models of Steady-State Genetic Algorithms
2 Steady-state evolutionary computation algorithms
3 Convergence of the Kr heuristic.
4 Continuous-time dynamical system models
5 Fixed points for random deletion
Mutation-Selection Algorithm: a Large Deviation Approach
2 MUTATION–SELECTION ALGORITHM
The Equilibrium and Transient Behavior of Mutation and Recombination
2 THE LIMITING DISTRIBUTION FOR MUTATION
3 THE LIMITING DISTRIBUTION FOR RECOMBINATION
4 THE LIMITING DISTRIBUTION FOR MUTATION AND RECOMBINATION
The Mixing Rate of Different Crossover Operators
APPENDIX: STATISTICS OF BLOCK SIZES
Dynamic Parameter Control in Simple Evolutionary Algorithms
3 DYNAMIC PARAMETER CONTROL IN SELECTION
4 DYNAMIC PARAMETER CONTROL IN MUTATION
Local Search and High Precision Gray Codes: Convergence Results and Neighborhoods
2 Local Optima, Problem Complexity, and Representation
3 Gray and Binary Representations
4 Properties of the Reflected Gray Space
5 High Precision Gray Codes and Local Optima
6 Combining Gray and Binary Neighborhoods
Burden and Benefits of Redundancy
3 INVESTIGATED PROBLEMS AND METHODOLOGY
4 ANALYSIS I: DEGREE OF REDUNDANCY
5 ANALYSIS II: MUTATION AND THE STRUCTURE OF LANDSCAPE