master > master: code py - config mit Bsp. von euklid + pollard

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RD 2022-07-01 13:49:59 +02:00
parent de238fede9
commit 278e3713c8

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@ -115,15 +115,86 @@
]
costs: [30, 10, 50, 10, 80]
values: [17, 14, 17, 5, 25]
- name: RUCKSACK
algorithm: BRANCH-AND-BOUND
max-cost: 900
items: [
'Sellerie',
'Sonnenblumenkerne',
'Rote Beete',
'Hirse',
'Buchweizen',
]
costs: [600, 100, 800, 100, 200]
values: [10, 15, 20, 5, 15]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Beispiele für Seminarwoche 12
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- name: RANDOM-WALK
algorithm: GRADIENT
one-based: true
coords-init: [3, 3]
landscape: &ref_landscape1
neighbourhoods:
radius: 1
# metric: MANHATTAN
metric: MAXIMUM
labels:
- x
- y
values:
- [5, 2, 1, 3, 4, 7]
- [8, 4, 3, 5, 5, 6]
- [9, 1, 2, 6, 8, 4]
- [7, 4, 4, 3, 7, 3]
- [6, 4, 2, 1, 0, 7]
- [4, 3, 5, 2, 1, 8]
optimise: MAX
- name: RANDOM-WALK
algorithm: ADAPTIVE
one-based: true
coords-init: [3, 3]
landscape: *ref_landscape1
optimise: MAX
- name: RANDOM-WALK
algorithm: METROPOLIS
annealing: false
temperature-init: 3.
one-based: true
coords-init: [5, 3]
landscape: *ref_landscape1
optimise: MAX
- name: RANDOM-WALK
algorithm: METROPOLIS
annealing: false
temperature-init: 3.
one-based: false
coords-init: [0]
landscape:
neighbourhoods:
radius: 1
metric: MANHATTAN
labels:
- x
values: [4, 6.5, 2]
optimise: MAX
- name: GENETIC
population:
- [3, 5, 4, 1, 6, 7, 2, 8, 9]
- [4, 5, 3, 2, 1, 6, 7, 8, 9]
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Beispiele für Seminarwoche 13
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
- name: EUKLID
numbers:
- 2017
- 58
- name: POLLARD-RHO
growth: LINEAR
# growth: EXPONENTIAL
number: 534767
x-init: 5