Basic Terms of Genetic Algorithms

Adaptive crossover
Adaptive mutation
Allele
Arithmetic crossover arithmetic crossover
Artificial life
Bin packing
Binary genes binary encoding gene
Boundary mutation Boundary Variation
Building Block hypothesis genetic block hypothesis and building block hypothesis
Cell
Character genes symbol encoding gene
Chromosome chromosome
Classifier System, CS Classifier System
Coarse-grained PGA coarse-grained parallel genetic algorithm
Coding individual code
Crossover crossover
Crossover Operator
Crossover rate crossover probability
Crowding
Cultural algorithms cultural Algorithm
Cut operator cut off Operator
Cycle crossover, CX cyclic crossover
Decode Decoding

Decomposition parallel approach factorization Parallel Algorithm
Deoxyribonucleic acid, DNA
Deterministic sampling deterministic sampling selection
Diploid
Dominance dominant genes
Dynamic Parameter encoding, DPE dynamic parameter Encoding
Edge recombination crossover, ex Side reconstruction crossover
Enumerative search enumeration Search Algorithm
Epistasis genetic concealment
Evaluation Function
Evolution
Evolution Strategy, es evolution strategy
Evolution Algorithms, EA Evolutionary Algorithm
Evolution computation evolutionary computing
Evolution programming, EP evolution plan
Expected Value Model Expected Value Selection Model
Fine-grained PGA fine-grained parallel genetic algorithm
Fitness fitness
Fitness Function fitness function
Fitness landscape fitness scene
Fitness scaling fitness Scaling
Floating-point genes floating point number encoding gene
Frequency of mutation variation frequency
Function optimization
Ga deceptive problem genetic algorithm Spoofing
Gaussian mutation Gaussian Variation
Gene
Generation Gap generation gap
Genetic algorithms, gas Genetic Algorithm

Genetic operators
Genetic Programming, GP Genetic Programming
Genetics genetics
Genome genome
Genotype genotype
Global searching global search
Gray Codes Gray Code
Greedy Algorithm
Hammig distance Hamming distance
Haploid
Heredity inheritance
Heterozygous
Heuristic Method Heuristic Algorithm
Hill-climbing search
Homozygous
Hybrid Genetic Algorithm, HGA Hybrid Genetic Algorithm
Hypercube
Implicit parallelism implicit concurrency
Individual individual
Initial population of initial population
Inverse Operator inverted Operator
Island model island Model
Knapsack Problem backpack Problems
Lethal gene lethal gene
Linear scaling linear scale transformation
Local searching local search
Locus
Machine Learning
Markov Chain

Massively PGA massively parallel genetic algorithm
Mating pairing
Mating rule matching rules
Messy GA, MGA messy Genetic Algorithm
Meta genetic algorithm meta-Genetic Algorithm
Michigan approach Michigan Method
Migration immigration
MIMD multi-instruction stream multi-data stream
Minimal deceptive problem, MDP minimum spoofing Problem
Multi-modal optimization multi-mode Optimization
Multi-object optimization Multi-objective Optimization
Multimodal Function
Multiparameter encoding multi-parameter Encoding
Multiple hump function multi-Peak Function
Multiple Point Crossover Multi-Point Crossover
Mutation Variation
Mutation Operator mutating Operator
Mutation Rate mutation probability
Neighborhood model neighbor Model
Artificial Neural Network, Ann Artificial Neural Network
Niche niche
Non-uniform mutation non-uniform Variation
Nondeterministic polynomial completeness NP-complete
Object Function
Off-line performance offline Performance
Offspring descendant group
On-line Performance Online Performance
One-point crossover Single Point Crossover
Optimization

Order crossover, Ox sequential crossover
Overspecification excessive descriptions
Parallel Genetic Algorithm, PGA Parallel Genetic Algorithm
Parallelism concurrency
Partially Mapped Crossover, PMx partial ing crossover
Partially matched crossover, PMx partially matched crossover
Penalty Function
Permutation Arrangement
Phenotype phenotype
Pitt approach Pitt Method
Plant pollination model plant pollination Model
Polyploid
Population Group
Population average fitness
Population diversity group diversity
Population size group size
Power Law Scaling multiplication power scale transformation
Premature Convergence early maturity and early convergence
Preselection pre-selection
Probabilistic algorithms probability algorithm
Probabilistic operator probability operator
Probability of crossover probability
Probability of inversion Probability
Probability of Mutation mutation probability
Proportional model proportional Selection Model
Random Algorithms random Algorithm
Random Searching and RS random search algorithms
Random Walks Random Walk
Rank-based model sort Selection Model

Read-coded genes floating point number encoding gene
Recessive recessive genes
Remainder stochastic sampling with replacement random selection without playback Remainder
Reordering operator reordering Operator
Reproduction Replication
Ribonucleic Acid, RNA
Robustness robustness
Roulette wheel selection
Scaling with Sigma truncation O ~ Truncation Scaling
Schema mode
Schema defining length schema definition Length
Schema Order Mode Level
Scheme theorem pattern Theorem
Selection
Selection operator
Sharing Function
SIMD single command stream multi-data stream
Simple genetic algorithm, basic SGA Genetic Algorithm
Basic variation of Simple Mutation
Simulated Annealing, sa Simulated Annealing Algorithm
Single hump function Single Peak Function
Splice operator concatenation operator
Standard parallel approach standard parallel method
Stepping-stone model stepping stone model
Stochastic sampling with replacement random selection without playback
Stochastic tournament model random League Selection Model
Termination conditions
Test function
Traveling Salesman Problem, TSP Traveling Salesman Problem
Two-Point Crossover dual-Point Crossover
Underspecification description is insufficient
Uniform crossover uniform crossover
Uniform mutation uniform Variation
X chromosome
Y chromosome