-
Notifications
You must be signed in to change notification settings - Fork 0
/
genome-worker.go
208 lines (180 loc) · 4.65 KB
/
genome-worker.go
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
package main
import (
"math/rand"
"time"
)
//Child is a child or genome with its fitness
type Child struct {
flow [][]int64
demand []int64
storage []int64
fitness int64
}
func (x *Child) toParent(c *Child) {
c.flow = x.flow
c.fitness = x.fitness
c.storage = x.storage
c.demand = x.demand
}
// mutateDumb: mutate the genome on all places but random place
// - mutates all
func (x *Child) mutateDumb(c *Child) {
c.flow = x.flow
c.fitness = x.fitness
c.storage = x.storage
c.demand = x.demand
localStorage := x.demand
for i, row := range x.flow {
for j := range row {
randomInt := rand.Int63n(10)
localStorage[i] = localStorage[i] - randomInt
localStorage[j] = localStorage[j] + randomInt
c.flow[i][j] = randomInt
}
}
}
// find a new Neighbour but still relatively random
// - only mutates one
func (x *Child) findNeighbourZero(c *Child) {
c.flow = x.flow
c.fitness = x.fitness
c.storage = x.storage
c.demand = x.demand
localStorage := x.demand
randomI := rand.Int63n(10)
randomJ := rand.Int63n(9)
randomInt := rand.Int63n(10)
c.flow[randomI][randomJ] = randomInt
for i := randomI; i >= 0; i-- {
for j := randomJ; j >= 0; j-- {
localStorage[i] = localStorage[i] - x.flow[i][j]
localStorage[j] = localStorage[j] + x.flow[i][j]
c.flow[i][j] = randomInt
}
}
c.storage = localStorage
if c.flow[randomI][randomJ] != randomInt {
}
}
// find a new Neighbour
// - mutates one randomly
// - corrects multiple after the mutated
// - recognize demand/storage
func (x *Child) findNeighbourOne(c *Child) {
c.flow = x.flow
c.fitness = 0
c.storage = x.storage
c.demand = x.demand
localStorage := c.storage
randomI := rand.Intn(10)
randomJ := rand.Intn(9)
for i := randomI; i >= 0; i-- {
for j := randomJ; j >= 0; j-- {
if localStorage[i] > 0 {
randomInt := rand.Int63n(localStorage[i])
localStorage[i] = localStorage[i] - randomInt
localStorage[j] = localStorage[j] + randomInt
c.flow[i][j] = randomInt
}
}
}
c.storage = localStorage
return
}
// find a new Neighbour
// - only mutates one
// - recognize demand/storage
// - only mutate existing edges
func (x *Child) findNeighbourTwo(c *Child, network [][]bool) {
c.flow = x.flow
c.fitness = 0
c.storage = x.storage
c.demand = x.demand
localStorage := c.storage
var edges [][]int
for i, row := range network {
for j, cell := range row {
if cell {
edge := []int{i, j}
edges = append(edges, edge)
}
}
}
randomEdge := edges[rand.Intn(len(edges))]
toChange := false
for i := range c.flow {
for j := range c.flow[i] {
if i == randomEdge[0] && j == randomEdge[1] {
toChange = true
}
tmp := localStorage[i] + c.flow[i][j]
if tmp > 0 && toChange {
randomInt := rand.Int63n(tmp)
localStorage[i] = localStorage[i] - randomInt
localStorage[j] = localStorage[j] + randomInt
c.flow[i][j] = randomInt
}
}
}
c.storage = localStorage
return
}
// initiateFlowDumb: this function generates a first very random genome
func (x *Child) initiateFlowZero(verticesCount int) {
var flowAll [][]int64
for i := 0; i < verticesCount; i++ {
var flowX []int64
for j := 0; j < (verticesCount - 1); j++ {
randomInt := rand.Int63n(10)
flowX = append(flowX, randomInt)
}
flowAll = append(flowAll, flowX)
}
x.flow = flowAll
return
}
// initiateFlowSmarter: this function generates a first flow
// - only uses existing vertices
func (x *Child) initiateFlowOne(verticesCount int, network [][]bool) {
localStorage := x.demand
for i := (verticesCount - 1); i >= 0; i-- {
var flowX []int64
for j := (verticesCount - 2); j >= 0; j-- {
if network[i][j] {
randomInt := rand.Int63n(10)
localStorage[i] = localStorage[i] - randomInt
localStorage[j] = localStorage[j] + randomInt
flowX = append(flowX, randomInt)
} else {
flowX = append(flowX, 0)
}
}
x.flow = append(x.flow, flowX)
}
x.storage = localStorage
return
}
// initiateFlowTwo: this function generates a first flow
// - only uses existing vertices
// - recognizes the demand
func (x *Child) initiateFlowTwo(verticesCount int, network [][]bool) {
r := rand.New(rand.NewSource(time.Now().UTC().UnixNano()))
localStorage := make([]int64, len(x.demand))
copy(localStorage, x.demand)
for i := (verticesCount - 1); i >= 0; i-- {
var flowX []int64
for j := (verticesCount - 2); j >= 0; j-- {
if network[i][j] && localStorage[i] > 0 {
randomInt := r.Int63n(localStorage[i])
localStorage[i] = localStorage[i] - randomInt
localStorage[j] = localStorage[j] + randomInt
flowX = append(flowX, randomInt)
} else {
flowX = append(flowX, 0)
}
}
x.flow = append(x.flow, flowX)
}
x.storage = localStorage
return
}