de

type ChampionIndividual struct {
	X DecisionVector
	C ConstraintVector
	F FitnessVector
}

type ConstraintVector []float64

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 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
	// 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
	// Problem is the current benchmarking function this population is attempting to optimise.
	Problem string
	// Dimen is the dimensionality of the problem being optimised.
	Dimen int
	// Seed is the value used to (re)init population.
	Seed uint64
}

type PopulationIndividual struct {
	CurX DecisionVector
	CurV DecisionVector
	CurC ConstraintVector
	CurF FitnessVector

	BestX DecisionVector
	BestC ConstraintVector
	BestF FitnessVector
}