Index
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G
- ga - Variable in class sfc_a.MainGA
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Genetic Algorithm
- ga - Variable in class sfc_b.Tuning
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object that implements the PAGA
- GA - Class in sfc_a
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Genetic Algorithm
- GA(int, int, int, int, int) - Constructor for class sfc_a.GA
- GA(Network) - Constructor for class sfc_a.GA
- GA_b - Class in sfc_b
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Parameter Adjustment Genetic Algorithm
- GA_b() - Constructor for class sfc_b.GA_b
- GA_b(int, int, int, String[], String[], double, double, int[][], String, VNFgraph, Network) - Constructor for class sfc_b.GA_b
- ga_c - Variable in class sfc_b.GA_b
- GA_c - Class in sfc_c
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Genetic Algorithm, used by PAGA for computing the fitness of candidate setups
- GA_c() - Constructor for class sfc_c.GA_c
- GA_c(int, int, int, int, int, VNFgraph, Network) - Constructor for class sfc_c.GA_c
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construct GA object on input of running parameters, virtual topology and substrate network
- GA.supergenthread - Class in sfc_a
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Thread for the parallel computation of the GA within supergenerations loop
- GA2 - Class in sfc_a
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Instance of the Genetic Algorithm for the parallel computation of supergenerations
- GA2(int, int, int, int, int) - Constructor for class sfc_a.GA2
- GA2(Network) - Constructor for class sfc_a.GA2
- genchromefit(Chromosome_b) - Method in class sfc_b.GA_b
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compute and store fitness of a single chromosome
- genefitness(Mapping) - Method in class sfc_a.GA
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compute fitness on given mapping
- genefitness(Mapping) - Method in class sfc_a.GA2
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compute fitness on given mapping
- genefitness(Mapping_c) - Method in class sfc_c.GA_c
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compute fitness on given mapping
- genefitness_experimental1(Mapping) - Method in class sfc_a.GA
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compute fitness on given mapping experimental version 1
- genefitness_experimental2(Mapping) - Method in class sfc_a.GA
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compute fitness on given mapping experimental version 2
- generations - Variable in class controller.Main
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GA parameters; populations size; supergenerations; crossover and mutation probabilities
- generations - Variable in class sfc_a.GA
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GA parameters
- generations - Variable in class sfc_a.GA2
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GA parameters
- generations - Variable in class sfc_a.MainGA
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Genetic Algorithm parameters
- generations - Variable in class sfc_a.Setup
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Genetic Algorithm parameter
- generations - Variable in class sfc_b.GA_b
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PAGA parameter, even numbers only
- generations - Variable in class sfc_b.Tuning
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PAGA parameter
- generations - Variable in class sfc_c.GA_c
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GA parameters
- genes - Variable in class sfc_a.MainGA
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Genetic Algorithm parameters
- genes - Variable in class sfc_b.Chromosome_b
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the phenotype of the chromosome
- genfit() - Method in class sfc_a.GA
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compute fitness for population
- genfit() - Method in class sfc_a.GA2
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compute fitness for population
- genfit() - Method in class sfc_b.GA_b
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computes and stores the fitness of each cromosome
- genfit() - Method in class sfc_c.GA_c
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compute fitness for population
- genfit1() - Method in class sfc_b.GA_b
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computes fitness for pop
- genfit1(Mapping[]) - Method in class sfc_a.GA
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compute and store fitness on chromosome
- genfit1(Mapping[]) - Method in class sfc_a.GA2
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compute and store fitness on chromosome
- genfit1(Mapping_c[]) - Method in class sfc_c.GA_c
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compute and store fitness on chromosome
- genfit2() - Method in class sfc_b.GA_b
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compute fitness for population generated by crossover and mutation (pop2)
- genpop() - Method in class sfc_a.GA2
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determines the nodes that will be used for population generation
- genpop() - Method in class sfc_b.GA_b
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determines the nodes that will be used for population generation
- genpop() - Method in class sfc_c.GA_c
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determines the nodes that will be used for population generation
- genpop_v1() - Method in class sfc_a.GA2
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generation of random population without the use of a heuristic
- genpop2() - Method in class sfc_a.GA2
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generates population
- genpop2() - Method in class sfc_c.GA_c
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generates population
- getavcap() - Method in class sfc_a.Best
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get available capacity
- getavcap() - Method in class sfc_a.Mapping
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get available capacity
- getavcap() - Method in class sfc_c.Best_c
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get available capacity
- getavcap() - Method in class sfc_c.Mapping_c
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get available capacity
- getbest() - Method in class sfc_a.GA2
- getbest2() - Method in class sfc_a.GA2
- getbestfitness() - Method in class sfc_a.Best
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get best fitness
- getbestfitness() - Method in class sfc_b.Best_b
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get best fitness
- getbestfitness() - Method in class sfc_c.Best_c
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get best fitness
- getbestmapping() - Method in class sfc_a.Best
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get best solution mapping
- getbestmapping() - Method in class sfc_c.Best_c
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get best solution mapping
- getbestphenotype() - Method in class sfc_b.Best_b
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get best solution
- getfitness() - Method in class sfc_a.Mapping
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get fitness
- getfitness() - Method in class sfc_b.Chromosome_b
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get fitness
- getfitness() - Method in class sfc_c.Mapping_c
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get fitness
- getgenes() - Method in class sfc_b.Chromosome_b
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get phenotype
- getlastfitness() - Method in class sfc_a.Last
-
get last best fitness
- getlastfitness() - Method in class sfc_c.Last_c
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get last best fitness
- getlastmapping() - Method in class sfc_a.Last
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get last best mapping
- getlastmapping() - Method in class sfc_c.Last_c
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get last best mapping
- getmapping() - Method in class sfc_a.Mapping
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get mapping
- getmapping() - Method in class sfc_c.Mapping_c
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get mapping
- getmnode(int) - Method in class sfc_a.Mapping
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get mapped node
- getpop() - Method in class sfc_a.GA2
- getpop2() - Method in class sfc_a.GA2
- getpops() - Method in class sfc_a.GA2
- getpops2() - Method in class sfc_a.GA2
- getSetup() - Method in class sfc_a.Setup
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get setup
- getstringmap() - Method in class sfc_a.Mapping
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mapping to string
- getstringmap() - Method in class sfc_b.Chromosome_b
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phenotype to string
- getstringmap() - Method in class sfc_c.Mapping_c
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mapping to string
- gettotaltime() - Method in class sfc_a.GA
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get runtime
- gettotaltime() - Method in class sfc_a.GA2
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get runtime
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