ga

type ChampionIndividual struct {
	X DecisionVector
}

type ConstraintVector []float64

type DE struct {
	// Generations denotes the number of generations the population evolves
	// for. Special value -1 disables limiting the number of generations.
	Generations int
	// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
	BenchMinIters int
	// Dimensions to solve the problem for.
	Dimensions []int
	// F is the differential weight (mutation/weighting factor).
	F float64
	// CR is the crossover probability constant.
	CR float64
	// MutationStrategy selects the mutation strategy, i.e. the variant of the
	// DE algorithm (0..17), see mutationStrategies.go for more details.
	MutationStrategy int
	// NP is the initial population size.
	NP int
	// BenchName is a name of the problem to optimise.
	BenchName string
	// contains filtered or unexported fields
}

type DecisionVector []float64

type FitnessVector []float64

type JDE struct {
	// Generations denotes the number of generations the population evolves
	// for. Special value -1 disables limiting the number of generations.
	Generations int
	// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
	BenchMinIters int
	// Dimensions to solve the problem for.
	Dimensions []int
	// F is the initial value of the differential weight (mutation/weighting factor).
	F float64
	// CR is the initial value of the crossover probability constant.
	CR float64

	// MutationStrategy selects the mutation strategy, i.e. the variant of the
	// jDE algorithm (0..17), see mutationStrategies.go for more details.
	MutationStrategy int
	// AdptScheme is the parameter self-adaptation scheme (0..1).
	AdptScheme int
	// NP is the initial population size.
	NP int
	// BenchName is a name of the problem to optimise.
	BenchName string
	// contains filtered or unexported fields
}

type Population struct {
	// Population is a slice of population individuals.
	Population []PopulationIndividual
	// Champion represents the best individual of the population.
	Champion ChampionIndividual
	// Problem is the current benchmarking function this population is attempting to optimise.
	Problem string
	// ProblemFunction is the actual function to optimise.
	ProblemFunc func([]float64) float64
	// Dimen is the dimensionality of the problem being optimised.
	Dimen int
	// Seed is the value used to (re)init population.
	Seed uint64

	// BestF is the best recorded value of the differential weight F.
	BestF float64
	// BestCR is the best recorded value of the differential weight CR.
	BestCR float64
	// CurF is the current value of F.
	CurF float64
	// CurCR is the current value of the differential weight CR.
	CurCR float64
	// contains filtered or unexported fields
}

type PopulationIndividual struct {
	CurX DecisionVector
	CurF FitnessVector

	BestX DecisionVector
	BestC ConstraintVector
	BestF FitnessVector
}

type SOMAT3A struct {
	// Generations is the number of generations to evolve for. Disable limit
	// with -1.
	Generations int
	// BenchMinIters is the number of iterations that the bench function will
	// be re-run.
	BenchMinIters int
	// Dimensions in which to look for a solution.
	Dimensions []int
	// NP is the initial population size.
	NP int
	// K is the number of individuals to choose the leader from.
	K int
	// M denotes how many individuals are picked from the population to form a
	// `team` during the organisation phase, can be thought of as "team size".
	M int
	// N is the number of best individuals in each team selected for actual
	// migration.
	N int
	// Njumps is the fixed number of jumps that each chosen migrating
	// individual performs on their way to the leader.
	Njumps int
	// BenchName is the human-friendly name of the benchmarking function.
	BenchName string
	// contains filtered or unexported fields
}

type SOMAT3AChampionIndividual struct {
	X DecisionVector
}

type SOMAT3APopulation struct {
	// Population is a slice of population individuals.
	Population []SOMAT3APopulationIndividual
	// Champion represents the best individual of the population.
	Champion SOMAT3AChampionIndividual
	// Problem is the current benchmarking function this population is attempting to optimise.
	Problem string
	// ProblemFunction is the actual function to optimise.
	ProblemFunc func([]float64) float64
	// Dimen is the dimensionality of the problem being optimised.
	Dimen int

	// Seed is the value used to (re)init population.
	Seed uint64

	// PRT is the perturbation parameter.
	PRT float64
	// K is the number of individuals to choose the leader from.
	K int
	// M denotes how many individuals are picked from the population to form a
	// `team` during the organisation phase, can be thought of as "team size".
	M int
	// N is the number of best individuals in each team selected for actual
	// migration.
	N int
	// Njumps is the fixed number of jumps that each chosen migrating
	// individual performs on their way to the leader.
	Njumps int
	// contains filtered or unexported fields
}

type SOMAT3APopulationIndividual struct {
	CurX      DecisionVector
	PRTVector []float64
	Jumps     [][]float64
}