diff --git a/algo/cec2020/doc.go b/algo/cec2020/doc.go
deleted file mode 100644
index 3a174eb..0000000
--- a/algo/cec2020/doc.go
+++ /dev/null
@@ -1,5 +0,0 @@
-// Copyright 2023 wanderer <a_mirre at utb dot cz>
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-// Package cec2020 implements algorithms required by CEC2020: jDE and T3A.
-package cec2020
diff --git a/algo/cec2020/jDE.go b/algo/cec2020/jDE.go
deleted file mode 100644
index 9ab4c73..0000000
--- a/algo/cec2020/jDE.go
+++ /dev/null
@@ -1,500 +0,0 @@
-// Copyright 2023 wanderer <a_mirre at utb dot cz>
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-package cec2020
-
-import (
-	"sort"
-	"time"
-
-	"golang.org/x/exp/rand"
-
-	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
-	"git.dotya.ml/wanderer/math-optim/stats"
-	"gonum.org/v1/gonum/stat/distuv"
-)
-
-// JDE is a holder for the settings of an instance of a self-adapting
-// differential evolution (jDE) algorithm.
-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
-	// rngF is a random number generator for differential weight (mutation/weighting factor).
-	rngF distuv.Uniform
-	// cr is a random number generator for crossover probability constant adapted over time.
-	rngCR distuv.Uniform
-	// 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
-	// ch is a channel for writing back computed results.
-	ch chan []stats.Stats
-	// chAlgoMeans is a channel for writing back algo means.
-	chAlgoMeans chan *stats.AlgoBenchMean
-	// initialised denotes the initialisation state of the struct.
-	initialised bool
-}
-
-const (
-	// jDEMinNP is the minimum size of the initial population for jDE.
-	// for jDE PaGMO specifies 8.
-	jDEMinNP = 4
-	// fMin is the minimum allowed value of the differential weight.
-	fMin = 0.36
-	// fMax is the maximum allowed value of the differential weight.
-	fMax = 1.0
-	// crMin is the minimum allowed value of the crossover probability constant.
-	crMin = 0.0
-	// crMax is the maximum allowed value of the crossover probability constant.
-	crMax = 1.0
-
-	// tau1 is used in parameter self-adaptation.
-	tau1 = 0.1
-	// tau2 is used in parameter self-adaptation.
-	tau2 = 0.1
-	// fl is used in parameter self-adaptation.
-	fl = 0.1
-	// fu is used in parameter self-adaptation.
-	fu = 0.9
-)
-
-// Init initialises the jDE algorithm, performs sanity checks on the inputs.
-func (j *JDE) Init(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
-	if j == nil {
-		cec2020Logger.Fatalln("jDE needs to be initialised before calling RunjDE, exiting...")
-	}
-
-	// check input parameters.
-	switch {
-	case generations == 0:
-		cec2020Logger.Fatalln("Generations cannot be 0, got", generations)
-
-	case generations == -1:
-		cec2020Logger.Println("Generations is '-1', disabling generation limits..")
-
-	case benchMinIters < 1:
-		cec2020Logger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
-
-	case mutStrategy < 0 || mutStrategy > 17:
-		cec2020Logger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
-
-	case adptScheme < 0 || adptScheme > 1:
-		cec2020Logger.Fatalln("Parameter self-adaptation scheme needs to be from the interval <0; 1>, got", adptScheme)
-
-	case np < jDEMinNP:
-		cec2020Logger.Fatalf("NP cannot be less than %d, got: %d\n.", jDEMinNP, np)
-
-	case f < fMin || f > fMax:
-		cec2020Logger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMin, fMax, f)
-
-	case cr < crMin || cr > crMax:
-		cec2020Logger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMin, crMax, cr)
-
-	case len(dimensions) == 0:
-		cec2020Logger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
-
-	case bench == "":
-		cec2020Logger.Fatalln("Bench cannot be empty, got:", bench)
-	}
-
-	j.Generations = generations
-	j.BenchMinIters = benchMinIters
-	j.MutationStrategy = mutStrategy
-	j.AdptScheme = adptScheme
-	j.NP = np
-	j.F = f
-	j.CR = cr
-
-	rngsrc := rand.NewSource(uint64(rand.Int63()))
-
-	j.rngF = distuv.Uniform{Min: fMin, Max: fMax, Src: rngsrc}
-	j.rngCR = distuv.Uniform{Min: crMin, Max: crMax, Src: rngsrc}
-
-	j.Dimensions = dimensions
-	j.BenchName = bench
-	j.ch = ch
-	j.chAlgoMeans = chAlgoMeans
-
-	j.initialised = true
-
-	cec2020Logger.Printf("jDE init done, jDE:%+v", j)
-}
-
-// InitAndRun initialises the jDE algorithm, performs sanity checks on the
-// inputs and calls the Run method.
-func (j *JDE) InitAndRun(generations, benchMinIters, mutStrategy, adptScheme, np int, f, cr float64, dimensions []int, bench string, ch chan []stats.Stats, chAlgoMeans chan *stats.AlgoBenchMean) {
-	if j == nil {
-		cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
-	}
-
-	j.Init(generations, benchMinIters, mutStrategy, adptScheme, np, f, cr, dimensions, bench, ch, chAlgoMeans)
-
-	j.Run()
-}
-
-// Run self-adapting differential evolution algorithm.
-func (j *JDE) Run() {
-	if j == nil {
-		cec2020Logger.Fatalln("jDE is nil, NewjDE() needs to be called first. exiting...")
-	}
-
-	if !j.initialised {
-		cec2020Logger.Fatalln("jDE needs to be initialised before calling Run(), exiting...")
-	}
-
-	var jDEStats []stats.Stats
-
-	jDEMeans := &stats.AlgoBenchMean{
-		Algo:       "jDE",
-		BenchMeans: make([]stats.BenchMean, 0, len(j.Dimensions)),
-	}
-
-	// run evolve for all dimensions.
-	for _, dim := range j.Dimensions {
-		maxFES := cec2020.GetMaxFES(dim)
-		fesPerIter := int(float64(maxFES / j.NP))
-		jDEStatDimX := &stats.Stats{
-			Algo:        "jDE",
-			Dimens:      dim,
-			Iterations:  j.BenchMinIters,
-			Generations: maxFES,
-			NP:          j.NP,
-			F:           j.F,
-			CR:          j.CR,
-		}
-		funcStats := &stats.FuncStats{BenchName: j.BenchName}
-		dimXMean := &stats.BenchMean{
-			Bench:       j.BenchName,
-			Dimens:      dim,
-			Iterations:  j.BenchMinIters,
-			Generations: maxFES,
-			Neighbours:  -1,
-		}
-		uniDist := distuv.Uniform{
-			Min: cec2020.SearchRange.Min(),
-			Max: cec2020.SearchRange.Max(),
-		}
-
-		if maxFES == -1 {
-			cec2020Logger.Fatalf("could not get maxFES for current dim (%d), bailing", dim)
-		}
-
-		cec2020Logger.Printf("running bench \"%s\" for %dD, maxFES: %d\n",
-			j.BenchName, dim, maxFES,
-		)
-
-		funcStats.BenchResults = make([]stats.BenchRound, j.BenchMinIters)
-
-		// rand.Seed(uint64(time.Now().UnixNano()))
-		// create and seed a source of pseudo-randomness
-		src := rand.NewSource(uint64(rand.Int63()))
-
-		// track execution duration.
-		start := time.Now()
-
-		for iter := 0; iter < j.BenchMinIters; iter++ {
-			cec2020Logger.Printf("run: %d, bench: %s, %dD, started at %s",
-				iter, j.BenchName, dim, start,
-			)
-
-			funcStats.BenchResults[iter].Iteration = iter
-
-			uniDist.Src = src
-
-			// create a population with known params.
-			pop := newPopulation(j.BenchName, j.NP, dim)
-
-			// set population seed.
-			pop.Seed = uint64(time.Now().UnixNano())
-			// initialise the population.
-			pop.Init()
-
-			var bestResult float64
-
-			// the core.
-			for i := 0; i < fesPerIter; i++ {
-				r := j.evaluate(pop)
-
-				// distinguish the first or any of the subsequent iterations.
-				switch i {
-				case 0:
-					bestResult = r
-
-				default:
-					// call evolve where jDE runs.
-					j.evolve(pop, &uniDist)
-
-					// take measurements of current population fitness.
-					r = j.evaluate(pop)
-
-					// save if better.
-					if r < bestResult {
-						bestResult = r
-					}
-				}
-
-				funcStats.BenchResults[iter].Results = append(
-					funcStats.BenchResults[iter].Results,
-					bestResult,
-				)
-				// this block makes sure we properly count func evaluations for
-				// the purpose of correctly comparable plot comparison. i.e.
-				// append the winning (current best) value NP-1 (the first best
-				// is already saved at this point) times to represent the fact
-				// that while evaluating (and comparing) other population
-				// individuals to the current best value is taking place in the
-				// background, the current best value itself is kept around and
-				// symbolically saved as the best of the Generation.
-				for x := 0; x < j.NP-1; x++ {
-					funcStats.BenchResults[iter].Results = append(
-						funcStats.BenchResults[iter].Results,
-						bestResult,
-					)
-				}
-			}
-		}
-
-		elapsed := time.Since(start)
-
-		cec2020Logger.Printf("completed: bench: %s, %dD, computing took %s\n",
-			j.BenchName, dim, elapsed,
-		)
-
-		// get mean vals.
-		dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
-		funcStats.MeanVals = dimXMean.MeanVals
-
-		jDEStatDimX.BenchFuncStats = append(
-			jDEStatDimX.BenchFuncStats, *funcStats,
-		)
-
-		jDEStats = append(jDEStats, *jDEStatDimX)
-
-		jDEMeans.BenchMeans = append(jDEMeans.BenchMeans, *dimXMean)
-	}
-
-	sort.Sort(jDEMeans)
-
-	j.chAlgoMeans <- jDEMeans
-	j.ch <- jDEStats
-}
-
-// evaluate evaluates the fitness of current population.
-func (j *JDE) evaluate(pop *Population) float64 {
-	f := cec2020.Functions[pop.Problem]
-
-	bestIndividual := pop.Population[pop.GetBestIdx()].CurX
-	bestSolution := f(bestIndividual)
-
-	return bestSolution
-}
-
-// evolve evolves a population by running the jDE (self-adapting Differential
-// Evolution) algorithm on the passed population until termination conditions
-// are met.
-// nolint: gocognit
-func (j *JDE) evolve(pop *Population, uniDist *distuv.Uniform) {
-	popCount := len(pop.Population)
-
-	for i, currentIndividual := range pop.Population {
-		idcs := make([]int, 0, popCount-1)
-		// idcs := make([]int, popCount-1)
-
-		// gather indices.
-		for k := 0; k < popCount; k++ {
-			if k != i {
-				idcs = append(idcs, k)
-			}
-		}
-
-		// randomly choose 3 of those idcs.
-		selectedIdcs := make([]int, 0)
-
-		selectedA := false
-		selectedB := false
-		selectedC := false
-
-		for !selectedA {
-			candidateA := rand.Intn(len(idcs)) % len(idcs)
-
-			if candidateA != i {
-				selectedIdcs = append(selectedIdcs, candidateA)
-				selectedA = true
-			}
-		}
-
-		for !selectedB {
-			a := selectedIdcs[0]
-			candidateB := rand.Intn(len(idcs)) % len(idcs)
-
-			if candidateB != i && candidateB != a {
-				selectedIdcs = append(selectedIdcs, candidateB)
-				selectedB = true
-			}
-		}
-
-		for !selectedC {
-			a := selectedIdcs[0]
-			b := selectedIdcs[1]
-			candidateC := rand.Intn(len(idcs)) % len(idcs)
-
-			if candidateC != i && candidateC != a && candidateC != b {
-				selectedIdcs = append(selectedIdcs, candidateC)
-				selectedC = true
-			}
-		}
-
-		// selected contains the selected population individuals.
-		selected := make([]PopulationIndividual, 0)
-
-		// select individuals for rand/1/bin
-		for _, idx := range selectedIdcs {
-			for k := 0; k < popCount; k++ {
-				if k == idx {
-					selected = append(selected, pop.Population[idx])
-				}
-			}
-		}
-
-		mutant := make([]float64, pop.Dimen)
-
-		// mutate.
-		for k := 0; k < pop.Dimen; k++ {
-			// mutant = a + mut * (b - c)
-			mutant[k] = selected[0].CurX[k] + (pop.BestF * (selected[1].CurX[k] - selected[2].CurX[k]))
-
-			// clip values to <0;1>.
-			switch {
-			case mutant[k] < 0:
-				mutant[k] = 0
-
-			case mutant[k] > 1:
-				mutant[k] = 1
-			}
-		}
-
-		crossPoints := make([]bool, pop.Dimen)
-
-		// prepare crossover points (binomial crossover).
-		for k := 0; k < pop.Dimen; k++ {
-			if v := uniDist.Rand(); v < pop.BestCR {
-				crossPoints[k] = true
-			} else {
-				crossPoints[k] = false
-			}
-		}
-
-		trial := make([]float64, pop.Dimen)
-
-		// recombine using crossover points.
-		for k := 0; k < pop.Dimen; k++ {
-			if crossPoints[k] {
-				trial[k] = currentIndividual.CurX[k]
-			} else {
-				trial[k] = mutant[k]
-			}
-		}
-
-		f := cec2020.Functions[pop.Problem]
-		currentFitness := f(currentIndividual.CurX)
-		trialFitness := f(trial)
-
-		// replace if better.
-		if trialFitness < currentFitness {
-			pop.Population[i].CurX = trial
-		}
-	}
-
-	// adapt parameters.
-	j.adaptParameters(pop)
-}
-
-// adptParameters adapts parameters F and CR based on
-// https://labraj.feri.um.si/images/0/05/CEC09_slides_Brest.pdf.
-func (j *JDE) adaptParameters(p *Population) {
-	var nuF float64
-
-	var nuCR float64
-
-	switch {
-	case j.AdptScheme == 0:
-		if rand2 := j.getRandF(); rand2 < tau1 {
-			nuF = fl + (j.getRandF() * fu)
-		} else {
-			nuF = p.f[len(p.f)-1]
-		}
-
-		if rand4 := j.getRandCR(); rand4 < tau2 {
-			nuCR = j.getRandCR()
-		} else {
-			nuCR = p.cr[len(p.cr)-1]
-		}
-
-	case j.AdptScheme == 1:
-		nuF = p.BestF + j.getRandF()*0.5
-		nuCR = p.BestCR + j.getRandCR()*0.5
-	}
-
-	// sort out F.
-	p.f = append(p.f, nuF)
-	// sort out CR
-	p.cr = append(p.cr, nuCR)
-
-	// update best, if improved.
-	switch {
-	case nuF < p.BestF:
-		p.BestF = nuF
-
-	case nuCR < p.BestCR:
-		p.BestCR = nuCR
-	}
-}
-
-// getRandF returns a random value of F for parameter self-adaptation.
-func (j *JDE) getRandF() float64 {
-	return j.rngF.Rand()
-}
-
-// getRandCR returns a random value of CR for parameter self-adaptation.
-func (j *JDE) getRandCR() float64 {
-	return j.rngCR.Rand()
-}
-
-// NewjDE returns a pointer to a new, uninitialised jDE instance.
-func NewjDE() *JDE {
-	return &JDE{}
-}
-
-// LogPrintln wraps the jDE logger's Println func.
-func LogPrintln(v ...any) {
-	cec2020Logger.Println(v...)
-}
-
-// LogPrintf wraps the jDE logger's Printf func.
-func LogPrintf(s string, v ...any) {
-	cec2020Logger.Printf(s, v...)
-}
-
-// LogFatalln wraps the jDE logger's Fatalln func.
-func LogFatalln(s string) {
-	cec2020Logger.Fatalln(s)
-}
-
-// LogFatalf wraps the jDE logger's Fatalf func.
-func LogFatalf(s string, v ...any) {
-	cec2020Logger.Fatalf(s, v...)
-}
diff --git a/algo/cec2020/log.go b/algo/cec2020/log.go
deleted file mode 100644
index d6d1cb7..0000000
--- a/algo/cec2020/log.go
+++ /dev/null
@@ -1,12 +0,0 @@
-// Copyright 2023 wanderer <a_mirre at utb dot cz>
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-package cec2020
-
-import (
-	"log"
-	"os"
-)
-
-// cec2020Logger declares and initialises a "custom" CEC2020 logger.
-var cec2020Logger = log.New(os.Stderr, " *** ∁ cec2020:", log.Ldate|log.Ltime|log.Lshortfile)
diff --git a/algo/cec2020/population.go b/algo/cec2020/population.go
deleted file mode 100644
index 0af1931..0000000
--- a/algo/cec2020/population.go
+++ /dev/null
@@ -1,167 +0,0 @@
-// Copyright 2023 wanderer <a_mirre at utb dot cz>
-// SPDX-License-Identifier: GPL-3.0-or-later
-
-package cec2020
-
-import (
-	"git.dotya.ml/wanderer/math-optim/bench/cec2020"
-	"golang.org/x/exp/rand"
-
-	"gonum.org/v1/gonum/stat/distuv"
-)
-
-type (
-	// DecisionVector is a []float64 abstraction representing the decision vector.
-	DecisionVector []float64
-	// FitnessVector is a []float64 abstraction representing the fitness vector.
-	FitnessVector []float64
-	// ConstraintVector is a []float64 abstraction representing the constraint vector.
-	ConstraintVector []float64
-)
-
-// PopulationIndividual representats a single population individual.
-type PopulationIndividual struct {
-	CurX DecisionVector
-	CurV DecisionVector
-	CurC ConstraintVector
-	CurF FitnessVector
-
-	BestX DecisionVector
-	BestC ConstraintVector
-	BestF FitnessVector
-}
-
-// ChampionIndividual is a representation of the best individual currently
-// available in the population.
-type ChampionIndividual struct {
-	X DecisionVector
-	C ConstraintVector
-	F FitnessVector
-}
-
-// Population groups population individuals (agents) with metadata about the population.
-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
-
-	// f is the differential weight (mutation/weighting factor) adapted over time.
-	f []float64
-	// cr is the crossover probability constant adapted over time.
-	cr []float64
-	// 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
-}
-
-// GetIndividual returns a reference to individual at position n.
-func (p *Population) GetIndividual(n uint) *PopulationIndividual { return &PopulationIndividual{} }
-
-// GetBestIdx returns the index of the best population individual.
-func (p *Population) GetBestIdx() int {
-	f := cec2020.Functions[p.Problem]
-
-	bestIndividual := 0
-	// the first one is the best one.
-	bestVal := f(p.Population[0].CurX)
-
-	for i, v := range p.Population {
-		current := f(v.CurX)
-
-		if current < bestVal {
-			bestIndividual = i
-		}
-	}
-
-	return bestIndividual
-}
-
-// GetWorstIdx returns the index of the worst population individual.
-func (p *Population) GetWorstIdx() int {
-	f := cec2020.Functions[p.Problem]
-
-	worstIndividual := 0
-	// the first one is the worst one.
-	worstVal := f(p.Population[0].CurX)
-
-	for i, v := range p.Population {
-		current := f(v.CurX)
-
-		if current > worstVal {
-			worstIndividual = i
-		}
-	}
-
-	return worstIndividual
-}
-
-// Init initialises all individuals to random values.
-func (p *Population) Init() {
-	uniform := distuv.Uniform{
-		Min: cec2020.SearchRange.Min(),
-		Max: cec2020.SearchRange.Max(),
-	}
-	uniform.Src = rand.NewSource(p.Seed)
-
-	for i, v := range p.Population {
-		v.CurX = make([]float64, p.Dimen)
-
-		for j := 0; j < p.Dimen; j++ {
-			v.CurX[j] = uniform.Rand()
-		}
-
-		p.Population[i] = v
-	}
-
-	p.f = make([]float64, p.Size())
-	p.cr = make([]float64, p.Size())
-}
-
-// Reinit reinitialises all individuals.
-func (p *Population) Reinit() {
-	p.Init()
-}
-
-// ReinitN reinitialises the individual at position n.
-func (p *Population) ReinitN(n uint) {}
-
-// Clear sets all vectors to 0.
-func (p *Population) Clear() {
-	if p.Population != nil {
-		for _, v := range p.Population {
-			v.CurX = make([]float64, p.Dimen)
-			v.CurC = make([]float64, p.Dimen)
-			v.CurF = make([]float64, p.Dimen)
-			v.CurV = make([]float64, p.Dimen)
-			v.BestX = make([]float64, p.Dimen)
-			v.BestC = make([]float64, p.Dimen)
-			v.BestF = make([]float64, p.Dimen)
-		}
-	}
-}
-
-// MeanVelocity computes the mean current velocity of all individuals in the population.
-func (p *Population) MeanVelocity() float64 { return 0.0 }
-
-// Size returns the number of population individuals.
-func (p *Population) Size() int { return len(p.Population) }
-
-// newPopulation returns a pointer to a new, uninitialised population.
-func newPopulation(benchProblem string, np, dimen int) *Population {
-	p := &Population{}
-
-	p.Problem = benchProblem
-
-	p.Dimen = dimen
-
-	// pre-alloc.
-	p.Population = make([]PopulationIndividual, np)
-
-	return p
-}