PanmicticEvolveScope

The best Chromosome in Population by fitness. Uses a StatisticsConfig.guaranteedSorted for optimization, except in the case of a call from DistributedEvolveScope.

The best fitness of Chromosome in Population. Uses a StatisticsConfig.guaranteedSorted for optimization, except in the case of a call from DistributedEvolveScope.

Number of elite chromosomes - the best chromosomes of the population, which have privileged rights:

Population Factory of GA.

Flag to stop evolution process cause stop condition has been worked.

Flag to stop evolution process cause the max iteration has been reached.

Fitness function - a function that evaluates the quality or "fitness" of each individual (chromosome) in a population. The fitness function determines how well a particular solution matches the target problem. It can be changed.

Current iteration of genetic algorithm.

Mean fitness value of chromosomes in Population

Median fitness value of chromosomes in Population

Population Name of GA.

Store parallelism configuration for GA. Determines genetic operators process.

Override base population as PanmicticPopulation for PanmicticGA.

Random associated with GA. Defines a pseudorandom number generator for predictive calculations.

Population Size of GA.

Statistics configuration associated with GA.

Store serves to synchronize between iterations of GA.

Duration between current and previous iteration.

Store for all TimeMarkers of GA.

The worst Chromosome in Population by fitness. Uses a StatisticsConfig.guaranteedSorted for optimization, except in the case of a call from DistributedEvolveScope.

The worst fitness of Chromosome in Population. Uses a StatisticsConfig.guaranteedSorted for optimization, except in the case of a call from DistributedEvolveScope.

Sets PanmicticPopulation.buffer = PanmicticPopulation.buffer + step

Sets PanmicticPopulation.size = PanmicticPopulation.size + step

Creates Statistic for best fitness of Chromosome in Population.

Performs a crossover step for population in PanmicticGA that modify in-place the input chromosomes.

Executes a blend crossover that modify in-place the input chromosomes. The blend crossover expects Chromosome.value of floating point numbers.

Executes a K point crossover on the input sequence individuals. The two values are modified in place.

Executes a one point crossover on the input sequence individuals. The two values are modified in place.

Executes an ordered crossover (OX) on the input chromosome values. The two value are modified in place. This crossover expects Chromosome.value as IntArray. Mixes indexes without conflicts, preserving parental relationships.

Executes a simulated binary crossover that modify in-place the input chromosomes. The simulated binary crossover expects Chromosome.value of floating point numbers.

Executes a simulated binary crossover that modify in-place the input chromosomes. The simulated binary crossover expects Chromosome.value of floating point numbers.

Executes a uniform crossover that modify in-place the input chromosomes. The attributes are swapped according to the chanceUniform probability.

Emit StatisticNote to StatisticsProvider.

Base evaluates function for all chromosomes in Population by fitnessFunction

Performs a fitness calculation step for chromosomes in the population.

If Population is not initialized - Fills a Population with randomly generated Chromosomes by Population.factory.

Executes CrossoverType.Iterative crossing.

Performs a mutation step for population in PanmicticGA that modify in-place the input chromosomes.

Flip the value of the attributes of the input chromosome. The chromosome is expected to be a BooleanArray. This mutation is usually applied on boolean individuals.

Applies a gaussian mutation of mean mean and standard deviation stddev on the input chromosome. This mutation expects Chromosome.value of floating point numbers.

Polynomial mutation as implemented in original NSGA-II algorithm in C by Deb. This mutation expects Chromosome.value of floating point numbers.

Executes a shuffle the attributes of the input chromosome. Usually this mutation is applied on vector of indices.

Executes a uniform mutation for the attributes of the input chromosome.

Iterative parallel process action from startIteration to endIteration.

Iterative parallel process action from startIteration to endIteration with step.

Process action with sequential or parallel mode.

Iterative process action from startIteration to endIteration.

Iterative process action from startIteration to endIteration with step.

Execute action with probability chance (0.0 < 1.0)

Executes CrossoverType.Randomly crossing.

Sets PanmicticPopulation.size and PanmicticPopulation.buffer of population.

Executes best selection step for Population.

Performs a selection step for population in PanmicticGA.

Performs a selection step for population in PanmicticGA. Suitable for selection mechanisms where behavior depends on indices. For example: selTournament

Executes random selection step for Population:

Executes roulette selection step for Population:

Executes tournament selection step for Population:

Executes worst selection step for Population.

Shake percent*100% population with Population.reset function.

Shake population with Population.reset function.

Creates Statistic for size of Population

Creates SingleStatisticNote and send it to GA.statisticsProvider

Creates MultiStatisticNote and send it to GA.statisticsProvider

Creates SingleStatisticNote and send it to GA.statisticsProvider Use registrar function

Stop and finish GA if stopCondition return true.

Stop and finish GA if iteration more or equal to maxIteration.

Stop and finish GA if:

Creates Statistic for current timeIteration.

Creates Statistic for worst fitness of Chromosome in Population.