412 lines
10 KiB
Go
412 lines
10 KiB
Go
// Copyright 2023 wanderer <a_mirre at utb dot cz>
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// SPDX-License-Identifier: GPL-3.0-or-later
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package ga
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import (
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"sort"
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"time"
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"golang.org/x/exp/rand"
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"git.dotya.ml/wanderer/math-optim/bench"
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"git.dotya.ml/wanderer/math-optim/stats"
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"gonum.org/v1/gonum/stat/distuv"
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)
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// DE is a holder for the settings of an instance of a Differential Evolution
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// (DE) algorithm.
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type DE struct {
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// Generations denotes the number of generations the population evolves
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// for. Special value -1 disables limiting the number of generations.
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Generations int
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// BenchMinIters is the number of iterations the bench function will be re-run (for statistical purposes).
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BenchMinIters int
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// Dimensions to solve the problem for.
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Dimensions []int
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// F is the differential weight (mutation/weighting factor).
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F float64
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// CR is the crossover probability constant.
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CR float64
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// MutationStrategy selects the mutation strategy, i.e. the variant of the
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// DE algorithm (0..17), see mutationStrategies.go for more details.
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MutationStrategy int
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// NP is the initial population size.
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NP int
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// BenchName is a name of the problem to optimise.
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BenchName string
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// ch is a channel for writing back computed results.
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ch chan []stats.Stats
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// chAlgoMeans is a channel for writing back algo means.
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chAlgoMeans chan *stats.AlgoBenchMean
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// initialised denotes the initialisation state of the struct.
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initialised bool
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}
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const (
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// MinNPDE is the minimum size of the initial population for DE.
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minNPDE = 4
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// fMin is the minimum allowed value of the differential weight.
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fMinDE = 0.5
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// fMax is the maximum allowed value of the differential weight.
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fMaxDE = 2.0
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// crMin is the minimum allowed value of the crossover probability constant.
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crMinDE = 0.2
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// crMax is the maximum allowed value of the crossover probability constant.
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crMaxDE = 0.9
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)
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// dELogger is a "custom" DE logger.
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var dELogger = newLogger(" *** δ DE:")
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// Init initialises the DE algorithm, performs sanity checks on the inputs.
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func (d *DE) Init(
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generations, benchMinIters, mutStrategy, np int,
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f, cr float64,
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dimensions []int,
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bench string,
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ch chan []stats.Stats,
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chAlgoMeans chan *stats.AlgoBenchMean,
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) {
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if d == nil {
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dELogger.Fatalln("DE needs to be initialised before calling Run(), exiting...")
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}
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// check input parameters.
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switch {
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case generations == 0:
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dELogger.Fatalln("Generations cannot be 0, got", generations)
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case generations == -1:
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dELogger.Println("Generations is '-1', disabling generation limits..")
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case benchMinIters < 1:
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dELogger.Fatalln("Minimum bench iterations cannot be less than 1, got:", benchMinIters)
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case mutStrategy < 0 || mutStrategy > 17:
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dELogger.Fatalln("Mutation strategy needs to be from the interval <0; 17>, got", mutStrategy)
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case np < minNPDE:
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dELogger.Fatalf("NP cannot be less than %d, got: %d\n.", minNPDE, np)
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case f < fMinDE || f > fMaxDE:
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dELogger.Fatalf("F needs to be from the interval <%f;%f>, got: %f\n.", fMinDE, fMaxDE, f)
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case cr < crMinDE || cr > crMaxDE:
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dELogger.Fatalf("CR needs to be from the interval <%f;>%f, got: %f\n.", crMinDE, crMaxDE, cr)
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case len(dimensions) == 0:
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dELogger.Fatalf("Dimensions cannot be empty, got: %+v\n", dimensions)
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case bench == "":
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dELogger.Fatalln("Bench cannot be unset, got:", bench)
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}
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d.Generations = generations
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d.BenchMinIters = benchMinIters
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d.MutationStrategy = mutStrategy
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d.NP = np
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d.F = f
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d.CR = cr
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d.Dimensions = dimensions
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d.BenchName = bench
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d.ch = ch
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d.chAlgoMeans = chAlgoMeans
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d.initialised = true
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}
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// InitAndRun initialises the DE algorithm, performs sanity checks on the
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// inputs and calls the Run method.
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func (d *DE) InitAndRun(
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generations, benchMinIters, mutStrategy, np int,
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f, cr float64,
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dimensions []int,
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bench string,
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ch chan []stats.Stats,
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chAlgoMeans chan *stats.AlgoBenchMean,
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) {
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if d == nil {
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dELogger.Fatalln("DE is nil, NewDE() needs to be called first. exiting...")
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}
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d.Init(
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generations, benchMinIters, mutStrategy, np,
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f, cr,
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dimensions,
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bench,
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ch,
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chAlgoMeans,
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)
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d.Run()
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}
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// Run self-adapting differential evolution algorithm.
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func (d *DE) Run() {
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if d == nil {
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dELogger.Fatalln("DE is nil, NewDE() needs to be called first. exiting...")
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}
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if !d.initialised {
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dELogger.Fatalln("DE needs to be initialised before calling Run(), exiting...")
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}
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var dEStats []stats.Stats
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dEMeans := &stats.AlgoBenchMean{
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Algo: "DE",
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BenchMeans: make([]stats.BenchMean, 0, len(d.Dimensions)),
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}
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// run evolve for for all dimensions.
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for _, dim := range d.Dimensions {
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maxFES := bench.GetGAMaxFES(dim)
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fesPerIter := int(float64(maxFES / d.NP))
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dEStatDimX := &stats.Stats{
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Algo: "DE",
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Dimens: dim,
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Iterations: d.BenchMinIters,
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Generations: maxFES,
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NP: d.NP,
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F: d.F,
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CR: d.CR,
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}
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funcStats := &stats.FuncStats{BenchName: d.BenchName}
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dimXMean := &stats.BenchMean{
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Bench: d.BenchName,
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Dimens: dim,
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Iterations: d.BenchMinIters,
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Generations: maxFES,
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Neighbours: -1,
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}
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benchFuncParams := bench.FunctionParams[d.BenchName]
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uniDist := distuv.Uniform{
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Min: benchFuncParams.Min(),
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Max: benchFuncParams.Max(),
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}
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funcStats.BenchResults = make([]stats.BenchRound, d.BenchMinIters)
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// rand.Seed(uint64(time.Now().UnixNano()))
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// create and seed a source of preudo-randomness
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src := rand.NewSource(uint64(rand.Int63()))
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// track execution duration.
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start := time.Now()
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for iter := 0; iter < d.BenchMinIters; iter++ {
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dELogger.Printf("run: %d, bench: %s, %dD, started at %s",
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iter, d.BenchName, dim, start,
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)
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funcStats.BenchResults[iter].Iteration = iter
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uniDist.Src = src
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// create a population with known params.
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pop := newPopulation(d.BenchName, d.NP, dim)
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// set population seed.
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pop.Seed = uint64(time.Now().UnixNano())
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// initialise the population.
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pop.Init()
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var bestResult float64
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// the core.
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for i := 0; i < fesPerIter; i++ {
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r := d.evaluate(pop)
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// distinguish the first or any of the subsequent iterations.
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switch i {
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case 0:
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bestResult = r
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default:
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// call evolve where actual DE runs.
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d.evolve(pop, &uniDist)
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// take measurements of current population fitness.
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r = d.evaluate(pop)
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// save if better.
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if r < bestResult {
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bestResult = r
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}
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}
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funcStats.BenchResults[iter].Results = append(
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funcStats.BenchResults[iter].Results,
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bestResult,
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)
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// this block makes sure we properly count func evaluations for
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// the purpose of correctly comparable plot comparison. i.e.
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// append the winning (current best) value NP-1 (the first best
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// is already saved at this point) times to represent the fact
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// that while evaluating (and comparing) other population
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// individuals to the current best value is taking place in the
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// background, the current best value itself is kept around and
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// symbolically saved as the best of the Generation.
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for x := 0; x < d.NP-1; x++ {
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funcStats.BenchResults[iter].Results = append(
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funcStats.BenchResults[iter].Results,
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bestResult,
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)
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}
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}
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}
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elapsed := time.Since(start)
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dELogger.Printf("completed: bench: %s, %dD, computing took %s\n",
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d.BenchName, dim, elapsed,
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)
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// get mean vals.
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dimXMean.MeanVals = stats.GetMeanVals(funcStats.BenchResults, maxFES)
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funcStats.MeanVals = dimXMean.MeanVals
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dEStatDimX.BenchFuncStats = append(
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dEStatDimX.BenchFuncStats, *funcStats,
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)
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dEStats = append(dEStats, *dEStatDimX)
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dEMeans.BenchMeans = append(dEMeans.BenchMeans, *dimXMean)
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}
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sort.Sort(dEMeans)
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d.chAlgoMeans <- dEMeans
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d.ch <- dEStats
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}
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// evaluate evaluates the fitness of current population.
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func (d *DE) evaluate(pop *Population) float64 {
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f := bench.Functions[pop.Problem]
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bestIndividual := pop.Population[pop.GetBestIdx()].CurX
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bestSolution := f(bestIndividual)
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return bestSolution
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}
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// evolve evolves a population by running the DE (Differential Evolution)
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// algorithm on the passed population until termination conditions are met.
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// nolint: gocognit
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func (d *DE) evolve(pop *Population, uniDist *distuv.Uniform) {
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popCount := len(pop.Population)
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for i, currentIndividual := range pop.Population {
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idcs := make([]int, 0, popCount-1)
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// gather indices.
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for k := 0; k < popCount; k++ {
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if k != i {
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idcs = append(idcs, k)
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}
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}
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// randomly choose 3 of those idcs.
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selectedIdcs := make([]int, 0)
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selectedA := false
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selectedB := false
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selectedC := false
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for !selectedA {
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candidateA := rand.Intn(len(idcs)) % len(idcs)
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if candidateA != i {
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selectedIdcs = append(selectedIdcs, candidateA)
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selectedA = true
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}
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}
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for !selectedB {
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a := selectedIdcs[0]
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candidateB := rand.Intn(len(idcs)) % len(idcs)
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if candidateB != i && candidateB != a {
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selectedIdcs = append(selectedIdcs, candidateB)
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selectedB = true
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}
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}
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for !selectedC {
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a := selectedIdcs[0]
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b := selectedIdcs[1]
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candidateC := rand.Intn(len(idcs)) % len(idcs)
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if candidateC != i && candidateC != a && candidateC != b {
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selectedIdcs = append(selectedIdcs, candidateC)
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selectedC = true
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}
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}
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// selected contains the selected population individuals.
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selected := make([]PopulationIndividual, 0)
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// select individuals for rand/1/bin
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for _, idx := range selectedIdcs {
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for k := 0; k < popCount; k++ {
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if k == idx {
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selected = append(selected, pop.Population[idx])
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}
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}
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}
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mutant := make([]float64, pop.Dimen)
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// mutate.
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for k := 0; k < pop.Dimen; k++ {
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// mutant = a + mut * (b - c)
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mutant[k] = selected[0].CurX[k] + (d.F * (selected[1].CurX[k] - selected[2].CurX[k]))
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// clip values to <0;1>.
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switch {
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case mutant[k] < 0:
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mutant[k] = 0
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case mutant[k] > 1:
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mutant[k] = 1
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}
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}
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crossPoints := make([]bool, pop.Dimen)
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// prepare crossover points (binomial crossover).
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for k := 0; k < pop.Dimen; k++ {
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if v := uniDist.Rand(); v < d.CR {
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crossPoints[k] = true
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} else {
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crossPoints[k] = false
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}
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}
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trial := make([]float64, pop.Dimen)
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// recombine using crossover points.
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for k := 0; k < pop.Dimen; k++ {
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if crossPoints[k] {
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trial[k] = currentIndividual.CurX[k]
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}
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}
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f := bench.Functions[pop.Problem]
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currentFitness := f(currentIndividual.CurX)
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trialFitness := f(trial)
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// replace if better.
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if trialFitness < currentFitness {
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pop.Population[i].CurX = trial
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}
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}
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}
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// NewDE returns a pointer to a new, uninitialised DE instance.
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func NewDE() *DE {
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return &DE{}
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}
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