示例#1
0
// MtIntOrd performs the mutation of genetic data from a ordered list of integers A
//  Output: modified individual 'A'
//  Note: using DM method as explained in [1] (citing [2])
//  References:
//   [1] Larrañaga P, Kuijpers CMH, Murga RH, Inza I and Dizdarevic S. Genetic Algorithms for the
//       Travelling Salesman Problem: A Review of Representations and Operators. Artificial
//       Intelligence Review, 13:129-170; 1999. doi:10.1023/A:1006529012972
//   [2] Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs. Berlin
//       Heidelberg: Springer Verlag; 1992
//       Joint Conference on Artificial Intelligence, 162-164; 1985.
//
//  DM displacement mutation method:
//   Ex:
//           0 1 2 3 4 5 6 7
//       A = a b c d e f g h   s = 2
//              ↑     ↑        t = 5
//              2     5
//
//       core = c d e  (subtour)  ncore = t - s = 5 - 2 = 3
//
//                0 1 2 3 4
//       remain = a b f g h  (remaining)  nrem = size - ncore = 8 - 3 = 5
//                       ↑
//                       4 = ins
func MtIntOrd(A []int, prms *Parameters) {
	size := len(A)
	if !rnd.FlipCoin(prms.IntPm) || size < 3 {
		if size == 2 {
			A[0], A[1] = A[1], A[0]
		}
		return
	}
	var indices []int
	var s, t, ncore, nrem, ins int
	if indices != nil {
		s, t, ins = indices[0], indices[1], indices[2]
		ncore = t - s
		nrem = size - ncore
	} else {
		s = rnd.Int(1, size-2)
		t = rnd.Int(s+1, size-1)
		ncore = t - s
		nrem = size - ncore
		ins = rnd.Int(1, nrem)
	}
	core := make([]int, ncore)
	remain := make([]int, nrem)
	var jc, jr int
	for i := 0; i < size; i++ {
		if i >= s && i < t {
			core[jc] = A[i]
			jc++
		} else {
			remain[jr] = A[i]
			jr++
		}
	}
	jc, jr = 0, 0
	for i := 0; i < size; i++ {
		if i < ins {
			A[i] = remain[jr]
			jr++
		} else {
			if jc < ncore {
				A[i] = core[jc]
				jc++
			} else {
				A[i] = remain[jr]
				jr++
			}
		}
	}
}
示例#2
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// CxIntOrd performs the crossover in a pair of individuals with integer numbers
// that correspond to a ordered sequence, e.g. for traveling salesman problem
//  Output:
//    a and b -- offspring chromosomes
//  Note: using OX1 method explained in [1] (proposed in [2])
//  References:
//   [1] Larrañaga P, Kuijpers CMH, Murga RH, Inza I and Dizdarevic S. Genetic Algorithms for the
//       Travelling Salesman Problem: A Review of Representations and Operators. Artificial
//       Intelligence Review, 13:129-170; 1999. doi:10.1023/A:1006529012972
//   [2] Davis L. Applying Adaptive Algorithms to Epistatic Domains. Proceedings of International
//       Joint Conference on Artificial Intelligence, 162-164; 1985.
//  Example:
//   data:
//         0 1   2 3 4   5 6 7
//     A = a b | c d e | f g h        size = 8
//     B = b d | f h g | e c a        cuts = [2, 5]
//             ↑       ↑       ↑      ends = [2, 5, 8]
//             2       5       8
//   first step: copy subtours
//     a = . . | f h g | . . .
//     b = . . | c d e | . . .
//   second step: copy unique from subtour's end, position 5
//               start adding here
//                       ↓                           5 6 7   0 1   2 3 4
//     a = d e | f h g | a b c         get from A: | f̶ g̶ h̶ | a b | c d e
//     b = h g | c d e | a b f         get from B: | e̶ c̶ a | b d̶ | f h g
func CxIntOrd(a, b, A, B []int, prms *Parameters) {
	size := len(A)
	if !rnd.FlipCoin(prms.IntPc) || size < 3 {
		for i := 0; i < len(A); i++ {
			a[i], b[i] = A[i], B[i]
		}
		return
	}
	var s, t int
	var cuts []int
	if len(cuts) == 2 {
		s, t = cuts[0], cuts[1]
	} else {
		s = rnd.Int(1, size-2)
		t = rnd.Int(s+1, size-1)
	}
	chk.IntAssertLessThan(s, t)
	acore := B[s:t]
	bcore := A[s:t]
	ncore := t - s
	acorehas := make(map[int]bool) // TODO: check if map can be replaced => improve efficiency
	bcorehas := make(map[int]bool)
	for i := 0; i < ncore; i++ {
		a[s+i] = acore[i]
		b[s+i] = bcore[i]
		acorehas[acore[i]] = true
		bcorehas[bcore[i]] = true
	}
	ja, jb := t, t
	for i := 0; i < size; i++ {
		k := (i + t) % size
		if !acorehas[A[k]] {
			a[ja] = A[k]
			ja++
			if ja == size {
				ja = 0
			}
		}
		if !bcorehas[B[k]] {
			b[jb] = B[k]
			jb++
			if jb == size {
				jb = 0
			}
		}
	}
	return
}
示例#3
0
// IntOrdMutation performs the mutation of genetic data from a ordered list of integers A
//  Output: modified individual 'A'
//  Note: using DM method as explained in [1] (citing [2])
//  References:
//   [1] Larrañaga P, Kuijpers CMH, Murga RH, Inza I and Dizdarevic S. Genetic Algorithms for the
//       Travelling Salesman Problem: A Review of Representations and Operators. Artificial
//       Intelligence Review, 13:129-170; 1999. doi:10.1023/A:1006529012972
//   [2] Michalewicz Z. Genetic Algorithms + Data Structures = Evolution Programs. Berlin
//       Heidelberg: Springer Verlag; 1992
//       Joint Conference on Artificial Intelligence, 162-164; 1985.
//
//  DM displacement mutation method:
//   Ex:
//           0 1 2 3 4 5 6 7
//       A = a b c d e f g h   s = 2
//              ↑     ↑        t = 5
//              2     5
//
//       core = c d e  (subtour)  ncore = t - s = 5 - 2 = 3
//
//                0 1 2 3 4
//       remain = a b f g h  (remaining)  nrem = size - ncore = 8 - 3 = 5
//                       ↑
//                       4 = ins
func IntOrdMutation(A []int, time int, ops *OpsData) {
	size := len(A)
	if !rnd.FlipCoin(ops.Pm) || size < 3 {
		if size == 2 {
			A[0], A[1] = A[1], A[0]
		}
		return
	}
	var s, t, ncore, nrem, ins int
	if ops.OrdSti != nil {
		s, t, ins = ops.OrdSti[0], ops.OrdSti[1], ops.OrdSti[2]
		ncore = t - s
		nrem = size - ncore
	} else {
		s = rnd.Int(1, size-2)
		t = rnd.Int(s+1, size-1)
		ncore = t - s
		nrem = size - ncore
		ins = rnd.Int(1, nrem)
	}
	core := make([]int, ncore)
	remain := make([]int, nrem)
	var jc, jr int
	for i := 0; i < size; i++ {
		if i >= s && i < t {
			core[jc] = A[i]
			jc++
		} else {
			remain[jr] = A[i]
			jr++
		}
	}
	jc, jr = 0, 0
	for i := 0; i < size; i++ {
		if i < ins {
			A[i] = remain[jr]
			jr++
		} else {
			if jc < ncore {
				A[i] = core[jc]
				jc++
			} else {
				A[i] = remain[jr]
				jr++
			}
		}
	}
}
示例#4
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// KeyMutation performs the mutation of genetic data from A
//  Output: modified individual 'A'
func KeyMutation(A []byte, time int, ops *OpsData) {
	size := len(A)
	if !rnd.FlipCoin(ops.Pm) || size < 1 {
		return
	}
	pos := rnd.IntGetUniqueN(0, size, ops.Nchanges)
	for _, i := range pos {
		v := rnd.Int(0, 100)
		A[i] = byte(v) // TODO: improve this
	}
}
示例#5
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// IntMutation performs the mutation of genetic data from A
//  Output: modified individual 'A'
func IntMutation(A []int, time int, ops *OpsData) {
	size := len(A)
	if !rnd.FlipCoin(ops.Pm) || size < 1 {
		return
	}
	pos := rnd.IntGetUniqueN(0, size, ops.Nchanges)
	for _, i := range pos {
		m := rnd.Int(1, int(ops.Mmax))
		if rnd.FlipCoin(0.5) {
			A[i] += m * A[i]
		} else {
			A[i] -= m * A[i]
		}
	}
}
示例#6
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// MtInt performs the mutation of genetic data from A
//  Output: modified individual 'A'
func MtInt(A []int, prms *Parameters) {
	size := len(A)
	if !rnd.FlipCoin(prms.IntPm) || size < 1 {
		return
	}
	mmax := 2
	pos := rnd.IntGetUniqueN(0, size, prms.IntNchanges)
	for _, i := range pos {
		m := rnd.Int(1, mmax)
		if rnd.FlipCoin(0.5) {
			A[i] += m * A[i]
		} else {
			A[i] -= m * A[i]
		}
	}
}
示例#7
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文件: optimiser.go 项目: cpmech/goga
// Solve solves optimisation problem
func (o *Optimiser) Solve() {

	// benchmark
	if o.Verbose {
		t0 := gotime.Now()
		defer func() {
			io.Pf("\nnfeval = %d\n", o.Nfeval)
			io.Pfblue2("cpu time = %v\n", gotime.Now().Sub(t0))
		}()
	}

	// output
	if o.Output != nil {
		o.Output(0, o.Solutions)
	}

	// perform evolution
	done := make(chan int, o.Ncpu)
	time := 0
	texc := time + o.DtExc
	for time < o.Tf {

		// run groups in parallel. up to exchange time
		for icpu := 0; icpu < o.Ncpu; icpu++ {
			go func(cpu int) {
				nfeval := 0
				for t := time; t < texc; t++ {
					if cpu == 0 && o.Verbose {
						io.Pf("time = %10d\r", t+1)
					}
					nfeval += o.EvolveOneGroup(cpu)
				}
				done <- nfeval
			}(icpu)
		}
		for cpu := 0; cpu < o.Ncpu; cpu++ {
			o.Nfeval += <-done
		}

		// compute metrics with all solutions included
		o.Metrics.Compute(o.Solutions)

		// exchange via tournament
		if o.Ncpu > 1 {
			if o.ExcTour {
				for i := 0; i < o.Ncpu; i++ {
					j := (i + 1) % o.Ncpu
					I := rnd.IntGetUnique(o.Groups[i].Indices, 2)
					J := rnd.IntGetUnique(o.Groups[j].Indices, 2)
					A, B := o.Groups[i].All[I[0]], o.Groups[i].All[I[1]]
					a, b := o.Groups[j].All[J[0]], o.Groups[j].All[J[1]]
					o.Tournament(A, B, a, b, o.Metrics)
				}
			}

			// exchange one randomly
			if o.ExcOne {
				rnd.IntGetGroups(o.cpupairs, utl.IntRange(o.Ncpu))
				for _, pair := range o.cpupairs {
					i, j := pair[0], pair[1]
					n := utl.Imin(o.Groups[i].Ncur, o.Groups[j].Ncur)
					k := rnd.Int(0, n)
					A := o.Groups[i].All[k]
					B := o.Groups[j].All[k]
					B.CopyInto(o.tmp)
					A.CopyInto(B)
					o.tmp.CopyInto(A)
				}
			}
		}

		// update time variables
		time += o.DtExc
		texc += o.DtExc
		time = utl.Imin(time, o.Tf)
		texc = utl.Imin(texc, o.Tf)

		// output
		if o.Output != nil {
			o.Output(time, o.Solutions)
		}
	}
}