Next: Preface

Glossary

Evolutionary Algorithms - Terms and Definitions¹

Version E-1.2

Hans-Georg Beyer $^{1)}$ , Eva Brucherseifer $^{2)}$ , Wilfried Jakob $^{3)}$ ,
Hartmut Pohlheim $^{4)}$ , Bernhard Sendhoff $^{5)}$ , and Thanh Binh To $^{6)}$

$^{1)}$ Universität Dortmund, Informatik XI (Systemanalyse), D-44221 Dortmund
$^{2)}$ TU Darmstadt, Fachgebiet für Regelungstheorie und Robotik, Landgraf-Georg-Str. 5, D-64283 Darmstadt
$^{3)}$ Forschungszentrum Karlsruhe, Institut für Angewandte Informatik, Postfach 3640, D-76021 Karlsruhe
$^{4)}$ DaimlerChrysler AG, Forschung und Technologie, Alt-Moabit 96a, D-10559 Berlin
$^{5)}$ Honda R&D Europe GmbH, Future Technology Research Division, Carl-Legien-Str. 30, D-63073 Offenbach/Main
$^{6)}$ Institut für Automation und Kommunikation e.V. Magdeburg, Steinfeldstr. 3, D-39179 Magdeburg

Preface
A
- Adaptation
- Allele
- Amelioration
- Automatically defined function (ADF)
B
- Baldwin effect
- Birth surplus
- Bit mutation/ bit flipping
- Bloat/ code bloat
- Block
- Building block hypothesis (BBH)
C
- Canonical GA
- (strong) Causality
- Chromosome
- Classifier system (CS)
- Coding
- Comma selection/ ``,''-selection
- (structural) Complexity
- Convergence
- Convergence velocity/
  Convergence order/
  Convergence rate
- Covariance matrix adaptation (CMA)
- Crossover (CO, XO)
- Crossover rate
D
- Derandomized ES
- Diffusion model
- Discrete recombination
- Distributed EA
- Diversity
- Dominant recombination
E
- Elitism/ elitist selection
- Endogenous strategy parameter
- Epistasis
- Evolutionary algorithm (EA)
- Evolutionary computation (EC)
- Evolutionary programming (EP)
- Evolution strategy (ES)
- Evolvable hardware (EH)
- Exogenous strategy parameter
F
- Fitness
- Fitness landscape
- Fitness-proportionate selection
- Function set
G
- Gene
- Gene convergence
- Gene pool
- Generation
- Generation gap
- Genetic algorithm (GA)
- Genetic drift
- Genetic operators
- Genetic programming (GP)
- Genetic repair
- Genome
- Genotype
- Genotypic variance
- Global population model
H
- Hybrid strategies
I
- Individual
- Intermediate recombination
- Introns
- Inversion
- Island model
L
- Lamarckian evolution
- Learning parameter
- Linear convergence
- Local population model
- Local search
M
- Meta EA
- Migration
- Multi-criteria optimization
- Multirecombination
- Mutation/ mutation operator
- Mutation rate
- Mutation strength
N
- (search) Neighborhood
- Neighborhood model
- (search) Neighborhood size
- Nested EA
- Neutral mutation
- Niching
- Node/block
O
- Objective function/ quality function
- Object parameter
- One-fifth-rule/
  1/5-rule
- Optimization
P
- Panmictic population
- Parent
- Pareto optimality
- Pareto set
- Performance
- Performance measures
- Permutation operator
- Phenotype
- Plus selection/ ``''-selection
- Pollination
- Population
- Population models
- Population size
- Predator-prey EA
- Premature convergence
- Problem size
- Program
- Progress rate
Q
- Quality gain
R
- Rank-based selection
- Real-coded GA
- Recombination
- Regional population model
- Replication
- Representation
- Roulette selection
S
- Scalability
- Scaling function
- Schema
- Schema theorems
- Search operator
- Search space
- Selection/ selection operator
- Selection pool
- Selection pressure
- Self-adaptation
- Self-adaptive behavior
- Steady-state EA
- Step length
- Stopping condition
- Strategy parameters
- Subpopulation
- Success probability
T
- Terminal set
- Termination condition
- Time complexity
- Truncation selection
V
- Variation/ variation operators
- Vector optimization

Hans-Georg Beyer 2002-02-25

Glossary

Evolutionary Algorithms - Terms and Definitions1

Version E-1.2

Evolutionary Algorithms - Terms and Definitions¹