GeneticAlgorithmMultiObjective
Library "GeneticAlgorithmMultiObjective"
Fast multi-objective genetic algorithm (NSGA-II) for Pine Script. All objectives are MAXIMIZED. Real-coded genes with SBX crossover and polynomial mutation, true Pareto fronts, per-front crowding distance, and elitist environmental selection.
createConfig(populationSize, geneCount, objectiveCount, minValues, maxValues, mutationRate, crossoverRate, elitismRate, tournamentSize, maxGenerations, etaCrossover, etaMutation, useSBX)
Creates a GA_Config.
Parameters:
populationSize (int)
geneCount (int)
objectiveCount (int)
minValues (array<float>)
maxValues (array<float>)
mutationRate (float)
crossoverRate (float)
elitismRate (float)
tournamentSize (int)
maxGenerations (int)
etaCrossover (float)
etaMutation (float)
useSBX (bool)
Returns: GA_Config
randomFloat(minVal, maxVal)
Random float in [minVal, maxVal).
Parameters:
minVal (float)
maxVal (float)
randomInt(minVal, maxVal)
Random integer in [minVal, maxVal) (upper bound excluded).
Parameters:
minVal (int)
maxVal (int)
createRandomChromosome(config)
Creates one random chromosome inside the gene bounds.
Parameters:
config (GA_Config)
initializePopulation(config)
Creates the initial random population.
Parameters:
config (GA_Config)
cloneChromosome(chrom)
Deep copy of a chromosome.
Parameters:
chrom (GA_Chromosome)
dominates(a, b)
Dominance test between two chromosomes (all objectives maximized).
Parameters:
a (GA_Chromosome)
b (GA_Chromosome)
rankPopulation(population, objectiveCount)
Fast non-dominated sorting (true Pareto fronts) plus crowding distance per front, in one pass. Sets rank and crowding on every chromosome. Call this before selection.
Parameters:
population (array<GA_Chromosome>): The evaluated population (objectives filled).
objectiveCount (int): Number of objectives.
Returns: The same array.
tournamentSelection(population, tournamentSize)
Tournament selection by rank, then crowding. Population must be ranked.
Parameters:
population (array<GA_Chromosome>)
tournamentSize (int)
selectBest(population, targetSize)
Picks the best targetSize individuals by (rank, crowding). Population must be ranked. Returns references (no copies).
Parameters:
population (array<GA_Chromosome>)
targetSize (int)
getParetoFront(population)
Returns the first Pareto front (rank 0). Population must be ranked.
Parameters:
population (array<GA_Chromosome>)
crossoverUniform(parent1, parent2, config)
Uniform crossover. Returns a new child.
Parameters:
parent1 (GA_Chromosome)
parent2 (GA_Chromosome)
config (GA_Config)
crossoverSBX(parent1, parent2, config)
Simulated Binary Crossover (SBX), bounded. Returns a new child.
Parameters:
parent1 (GA_Chromosome)
parent2 (GA_Chromosome)
config (GA_Config)
mutateUniform(chrom, config)
Random-reset mutation (in place).
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
mutatePolynomial(chrom, config)
Polynomial mutation (in place). Small local changes, much more accurate than random reset.
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
mutate(chrom, config)
Mutation chosen by config.useSBX.
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
createOffspring(population, config, count)
Creates `count` unevaluated children from a ranked population (call rankPopulation first, or use the output of environmentalSelection).
Parameters:
population (array<GA_Chromosome>)
config (GA_Config)
count (int)
environmentalSelection(combined, config)
True NSGA-II survivor selection. Pass parents + evaluated offspring merged in one array; returns the best populationSize individuals (ranked, with crowding set).
Parameters:
combined (array<GA_Chromosome>)
config (GA_Config)
createNextGeneration(population, config)
Simple generational step with elitism (compatible with the old API). The returned population has elites evaluated and new children with zero objectives (evaluate them before the next call).
Parameters:
population (array<GA_Chromosome>)
config (GA_Config)
GA_Chromosome
A candidate solution.
Fields:
genes (array<float>): Gene values.
objectives (array<float>): Objective values (all maximized). Filled by the user.
fitness (series float): Free slot for user data.
rank (series int): Pareto front index (0 = best front).
crowding (series float): Crowding distance inside its front (bigger = more isolated = better).
GA_Config
Algorithm settings.
Fields:
populationSize (series int): Number of individuals.
geneCount (series int): Number of genes per individual.
objectiveCount (series int): Number of objectives.
minValues (array<float>): Lower bound of each gene.
maxValues (array<float>): Upper bound of each gene.
mutationRate (series float): Per-gene mutation probability (a good default is 1 / geneCount).
crossoverRate (series float): Probability that two parents are recombined.
elitismRate (series float): Share of the population kept as elites in createNextGeneration.
tournamentSize (series int): Tournament size (2 = binary tournament).
maxGenerations (series int): Free slot for the caller's loop.
etaCrossover (series float): SBX distribution index (10 to 20; larger = children closer to parents).
etaMutation (series float): Polynomial mutation distribution index (15 to 30).
useSBX (series bool): true = SBX + polynomial mutation, false = uniform crossover + random reset.
Fast multi-objective genetic algorithm (NSGA-II) for Pine Script. All objectives are MAXIMIZED. Real-coded genes with SBX crossover and polynomial mutation, true Pareto fronts, per-front crowding distance, and elitist environmental selection.
createConfig(populationSize, geneCount, objectiveCount, minValues, maxValues, mutationRate, crossoverRate, elitismRate, tournamentSize, maxGenerations, etaCrossover, etaMutation, useSBX)
Creates a GA_Config.
Parameters:
populationSize (int)
geneCount (int)
objectiveCount (int)
minValues (array<float>)
maxValues (array<float>)
mutationRate (float)
crossoverRate (float)
elitismRate (float)
tournamentSize (int)
maxGenerations (int)
etaCrossover (float)
etaMutation (float)
useSBX (bool)
Returns: GA_Config
randomFloat(minVal, maxVal)
Random float in [minVal, maxVal).
Parameters:
minVal (float)
maxVal (float)
randomInt(minVal, maxVal)
Random integer in [minVal, maxVal) (upper bound excluded).
Parameters:
minVal (int)
maxVal (int)
createRandomChromosome(config)
Creates one random chromosome inside the gene bounds.
Parameters:
config (GA_Config)
initializePopulation(config)
Creates the initial random population.
Parameters:
config (GA_Config)
cloneChromosome(chrom)
Deep copy of a chromosome.
Parameters:
chrom (GA_Chromosome)
dominates(a, b)
Dominance test between two chromosomes (all objectives maximized).
Parameters:
a (GA_Chromosome)
b (GA_Chromosome)
rankPopulation(population, objectiveCount)
Fast non-dominated sorting (true Pareto fronts) plus crowding distance per front, in one pass. Sets rank and crowding on every chromosome. Call this before selection.
Parameters:
population (array<GA_Chromosome>): The evaluated population (objectives filled).
objectiveCount (int): Number of objectives.
Returns: The same array.
tournamentSelection(population, tournamentSize)
Tournament selection by rank, then crowding. Population must be ranked.
Parameters:
population (array<GA_Chromosome>)
tournamentSize (int)
selectBest(population, targetSize)
Picks the best targetSize individuals by (rank, crowding). Population must be ranked. Returns references (no copies).
Parameters:
population (array<GA_Chromosome>)
targetSize (int)
getParetoFront(population)
Returns the first Pareto front (rank 0). Population must be ranked.
Parameters:
population (array<GA_Chromosome>)
crossoverUniform(parent1, parent2, config)
Uniform crossover. Returns a new child.
Parameters:
parent1 (GA_Chromosome)
parent2 (GA_Chromosome)
config (GA_Config)
crossoverSBX(parent1, parent2, config)
Simulated Binary Crossover (SBX), bounded. Returns a new child.
Parameters:
parent1 (GA_Chromosome)
parent2 (GA_Chromosome)
config (GA_Config)
mutateUniform(chrom, config)
Random-reset mutation (in place).
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
mutatePolynomial(chrom, config)
Polynomial mutation (in place). Small local changes, much more accurate than random reset.
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
mutate(chrom, config)
Mutation chosen by config.useSBX.
Parameters:
chrom (GA_Chromosome)
config (GA_Config)
createOffspring(population, config, count)
Creates `count` unevaluated children from a ranked population (call rankPopulation first, or use the output of environmentalSelection).
Parameters:
population (array<GA_Chromosome>)
config (GA_Config)
count (int)
environmentalSelection(combined, config)
True NSGA-II survivor selection. Pass parents + evaluated offspring merged in one array; returns the best populationSize individuals (ranked, with crowding set).
Parameters:
combined (array<GA_Chromosome>)
config (GA_Config)
createNextGeneration(population, config)
Simple generational step with elitism (compatible with the old API). The returned population has elites evaluated and new children with zero objectives (evaluate them before the next call).
Parameters:
population (array<GA_Chromosome>)
config (GA_Config)
GA_Chromosome
A candidate solution.
Fields:
genes (array<float>): Gene values.
objectives (array<float>): Objective values (all maximized). Filled by the user.
fitness (series float): Free slot for user data.
rank (series int): Pareto front index (0 = best front).
crowding (series float): Crowding distance inside its front (bigger = more isolated = better).
GA_Config
Algorithm settings.
Fields:
populationSize (series int): Number of individuals.
geneCount (series int): Number of genes per individual.
objectiveCount (series int): Number of objectives.
minValues (array<float>): Lower bound of each gene.
maxValues (array<float>): Upper bound of each gene.
mutationRate (series float): Per-gene mutation probability (a good default is 1 / geneCount).
crossoverRate (series float): Probability that two parents are recombined.
elitismRate (series float): Share of the population kept as elites in createNextGeneration.
tournamentSize (series int): Tournament size (2 = binary tournament).
maxGenerations (series int): Free slot for the caller's loop.
etaCrossover (series float): SBX distribution index (10 to 20; larger = children closer to parents).
etaMutation (series float): Polynomial mutation distribution index (15 to 30).
useSBX (series bool): true = SBX + polynomial mutation, false = uniform crossover + random reset.
Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.
Отказ от ответственности
Информация и публикации не предназначены для предоставления и не являются финансовыми, инвестиционными, торговыми или другими видами советов или рекомендаций, предоставленных или одобренных TradingView. Подробнее читайте в Условиях использования.