A
Adaptive genetic algorithms (AGA)
implementation
multiple heuristic approach
simulated annealing
tabu search
performance improvements
encoding
fitness function design
fitness value hashing
mutation and crossover methods
parallel processing
Artificial intelligence
biological analogies
biological evolution
allele and locus
genotype and phenotype
terminology
evolutionary computation
advantages
features
evolution strategies
genetic algorithm
genetic representations
parameters
crossover rate
mutation rate
population size
search spaces
fitness landscapes
local optimums
termination conditions
Artificial neural networks
Asymmetric traveling salesman problem
B
Bit flip mutation
Boundary mutation
Brute force algorithm
C
Class scheduling
analysis and refinement
hard constraints
calcClashes method
createClasses method
evaluation
execution
initialization
initPopulation method
mutation
Professor class
termination
Timetable class
TimetableGA class
problems
soft constraints
D
Digital computers
E, F
Elitism
Evolutionary robotics
G, H
Gaussian mutation
Genetic algorithm
crossover methods
crossoverPopulation()
pseudo code
roulette wheel selection
selectParent()
uniform crossover
elitism
evaluation stage
population initialization
pre-implementation
classes
interfaces
I, J, K, L, M
IntStream
N, O
Nearest neighbor algorithm
P, Q
Permutation encoding
Pythagorean Theorem
R
Robotic controllers
encoding data
evaluation phase
calcFitness function
GeneticAlgorithm class
AllOnesGA class
RobotController
scoreRoute method
tournamentSize property
selection method and crossover
single point crossover
tournament selection
termination check
S
Simulated annealing
Single point crossover
Swap mutation
T
Tabu search
Tournament selection
Traveling salesman problem (TSP)
constraints
crossover
containsGene method
ordered crossover
selectParent method
GeneticAlgorithm object
U
Uniform mutation technique
V, W, X, Y, Z
Value hashing