├── .gitignore
├── Genetic Algorithm.py
├── Genetic Algorithms for Swarm Parameter Tuning.pdf
├── Genetic Algorithms for Swarm Parameter Tuning.py
├── LICENSE
├── README.md
├── parameters.json
└── scene
    ├── parameters.csv
    ├── scene.json
    ├── scene_default.json
    ├── scene_distances.json
    ├── scene_distancesPredator.json
    ├── scene_distancesPrey.json
    ├── scene_fast.json
    ├── scene_predators.json
    ├── scene_preys.json
    ├── scene_velocities.json
    └── scenes.csv


/.gitignore:
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117 | 


--------------------------------------------------------------------------------
/Genetic Algorithm.py:
--------------------------------------------------------------------------------
  1 | """Genetic Algorithm.py
  2 | 
  3 | Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, 
  4 | to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal
  5 | parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, 
  6 | and attempt to achieve a series of non-trivial swarm-level behaviours.
  7 | """
  8 | 
  9 | from __future__ import barry_as_FLUFL
 10 | 
 11 | __all__ = None
 12 | __author__ = "#CHEE JUN YUAN GLENN#"
 13 | __copyright__ = "Copyright © 2019, Cheejyg"
 14 | __email__ = "CHEE0124@e.ntu.edu.sg"
 15 | __license__ = "MIT"
 16 | __maintainer__ = "#CHEE JUN YUAN GLENN#"
 17 | __status__ = "Development"
 18 | __version__ = "1.0"
 19 | 
 20 | # import argparse
 21 | import json
 22 | # import math
 23 | # import matplotlib
 24 | # import matplotlib.animation
 25 | # import matplotlib.pyplot
 26 | # from mpl_toolkits.mplot3d import Axes3D
 27 | import multiprocessing
 28 | import numpy
 29 | # import os
 30 | import random
 31 | # import scipy
 32 | # import sys
 33 | # import tensorflow
 34 | import time
 35 | 
 36 | GeneticAlgorithmsForSwarmParameterTuning = __import__("Genetic Algorithms for Swarm Parameter Tuning")
 37 | 
 38 | random.seed(24)
 39 | numpy.random.seed(24)
 40 | search_space = 100
 41 | crossover_type = 2  # [0 = Uniform, 1 = Single-point, 2 = Two-point, k = k-point]
 42 | mutation_type = 0  # [0 = Bit, 1 = Flip, 2 = Boundary, 3 = Non-Uniform, 4 = Uniform, 5 = Gaussian, 6 = Shrink]
 43 | n = 100
 44 | nParents = 12
 45 | genes = 12
 46 | nSpecialisations = 6
 47 | α = 0.5
 48 | β = 0.1
 49 | generations = 10000
 50 | 
 51 | outputFilename = "Genetic Algorithm.log.json"
 52 | verbosity = 1
 53 | outputFile = None
 54 | output = None
 55 | 
 56 | scenes = None
 57 | specialisationScenes = [
 58 | 	"scene/scene_velocities.json", "scene/scene_predators.json", "scene/scene_preys.json", 
 59 | 	"scene/scene_distances.json", "scene/scene_distancesPredator.json", "scene/scene_distancesPrey.json"
 60 | ]
 61 | 
 62 | population = None
 63 | populationFitness = None
 64 | populationSpecialisation = None
 65 | childrenFitness = None
 66 | childrenSpecialisation = None
 67 | 
 68 | parameters = None
 69 | 
 70 | # multiprocessing
 71 | batch_size = int(multiprocessing.cpu_count() / 2)
 72 | process = None
 73 | processReturn = None
 74 | 
 75 | 
 76 | def __main__() -> None:
 77 | 	global outputFilename
 78 | 	global verbosity
 79 | 	global outputFile
 80 | 	global output
 81 | 	global population
 82 | 	global populationFitness
 83 | 	global populationSpecialisation
 84 | 	global childrenFitness
 85 | 	global childrenSpecialisation
 86 | 	global process
 87 | 	global processReturn
 88 | 	
 89 | 	children = []
 90 | 	childrenFitness = []
 91 | 	childrenSpecialisation = []
 92 | 	
 93 | 	process = []
 94 | 	processReturn = multiprocessing.Manager().list()
 95 | 	for x in range(n + (2 * nParents)):
 96 | 		processReturn.append([])
 97 | 	
 98 | 	output = []
 99 | 	outputFile = open(outputFilename, "at")
100 | 	outputFile.write("[")
101 | 	
102 | 	__initialise__()
103 | 	
104 | 	for generation in range(generations):
105 | 		population_fitness = numpy.copy(populationFitness)
106 | 		population_fitness[numpy.arange(len(population)), populationSpecialisation] = 0
107 | 		population_fitness = populationFitness - population_fitness
108 | 		population_fitness = population_fitness / numpy.sum(population_fitness, axis=0)  # normalise
109 | 		
110 | 		alpha_normalisation = numpy.zeros(population_fitness.shape, dtype=float)
111 | 		for specialisation in range(nSpecialisations):
112 | 			specialisation_population = numpy.where(populationSpecialisation == specialisation)[0]
113 | 			alpha_normalisation[specialisation_population, specialisation] += 1 / len(specialisation_population)
114 | 		population_fitness = (α * alpha_normalisation) + (1 - α) * population_fitness
115 | 		
116 | 		for parents in range(nParents):
117 | 			a_specialisation, b_specialisation = (
118 | 				random.randint(0, nSpecialisations - 1), random.randint(0, nSpecialisations - 1)
119 | 			)
120 | 			
121 | 			if a_specialisation != b_specialisation:
122 | 				a = numpy.random.choice(len(population), 1, p=population_fitness[:, a_specialisation])[0]
123 | 				b = numpy.random.choice(len(population), 1, p=population_fitness[:, b_specialisation])[0]
124 | 			else:
125 | 				a, b = numpy.random.choice(len(population), 2, False, p=population_fitness[:, a_specialisation])
126 | 			
127 | 			a, b = population[a], population[b]
128 | 			
129 | 			a, b = crossover(a, b)
130 | 			a, b = mutation(a), mutation(b)
131 | 			
132 | 			children.append(a), children.append(b)
133 | 			
134 | 			a_specialisation, b_specialisation = numpy.random.choice(
135 | 				numpy.array([random.randint(0, nSpecialisations - 1), a_specialisation, b_specialisation]), 2, True, 
136 | 				p=[β, (1 - β)/2, (1 - β)/2]
137 | 			)
138 | 			
139 | 			childrenSpecialisation.append(a_specialisation), childrenSpecialisation.append(b_specialisation)
140 | 			
141 | 			p1, p2 = multiprocessing.Process(
142 | 				target=__fitness_multiprocessing__, 
143 | 				args=(len(children) - 2, a, specialisationScenes[a_specialisation], processReturn)
144 | 			), multiprocessing.Process(
145 | 				target=__fitness_multiprocessing__, 
146 | 				args=(len(children) - 1, b, specialisationScenes[b_specialisation], processReturn)
147 | 			)
148 | 			process.append(p1), process.append(p2)
149 | 			p1.start(), p2.start()
150 | 			
151 | 			if (2 * parents) % batch_size == 0:
152 | 				for p in process:
153 | 					p.join()
154 | 		
155 | 		population = numpy.concatenate((population, children))
156 | 		populationSpecialisation = numpy.concatenate((populationSpecialisation, childrenSpecialisation))
157 | 		for p in process:
158 | 			p.join()
159 | 		childrenFitness = numpy.array(processReturn[:len(children)], copy=True)
160 | 		populationFitness = numpy.concatenate((populationFitness, childrenFitness))
161 | 		
162 | 		specialisation_remove = (numpy.unique(
163 | 			populationSpecialisation, return_index=False, return_inverse=False, return_counts=True
164 | 		)[1] * ((len(population) - n) / len(population))).astype(int)
165 | 		population_remove = []
166 | 		for specialisation in range(nSpecialisations):
167 | 			specialisation_population = numpy.where(populationSpecialisation == specialisation)[0]
168 | 			fitness_sort = numpy.argsort(populationFitness[specialisation_population, specialisation], axis=0)
169 | 			population_remove += (specialisation_population[fitness_sort][0:specialisation_remove[specialisation]])\
170 | 				.tolist()
171 | 		population = numpy.delete(population, population_remove, axis=0)
172 | 		populationFitness = numpy.delete(populationFitness, population_remove, axis=0)
173 | 		populationSpecialisation = numpy.delete(populationSpecialisation, population_remove, axis=0)
174 | 		
175 | 		children = []
176 | 		childrenFitness = []
177 | 		childrenSpecialisation = []
178 | 		
179 | 		output.append({
180 | 			"population": population.tolist(), 
181 | 			"specialisation": populationSpecialisation.tolist(), 
182 | 			"fitness": populationFitness.tolist()
183 | 		})
184 | 		
185 | 		if generation % 20 == 0:
186 | 			outputFile.write(json.dumps(output)[1:-1])
187 | 			outputFile.write(",")
188 | 			outputFile.flush()
189 | 			output = []
190 | 		
191 | 		if verbosity > 0:
192 | 			if verbosity == 1:
193 | 				print(
194 | 					"generation: %d, n: %d" % (generation, len(population))
195 | 				)
196 | 			elif verbosity == 2:
197 | 				print(
198 | 					"generation: %d, n: %d\npopulation: \t\t%s\nspecialisation: \t%s\nfitness: \t\t\t%s\n" 
199 | 					% (
200 | 						generation, len(population), population.tolist(), populationSpecialisation.tolist(), 
201 | 						populationFitness.tolist()
202 | 					)
203 | 				)
204 | 	outputFile.write(json.dumps(output)[1:-1])
205 | 	outputFile.write("]")
206 | 	outputFile.flush()
207 | 	outputFile.close()
208 | 	
209 | 	return
210 | 
211 | 
212 | def __initialise__() -> None:
213 | 	global population
214 | 	global populationFitness
215 | 	global populationSpecialisation
216 | 	global process
217 | 	global processReturn
218 | 	
219 | 	population = numpy.random.rand(n, genes) * search_space
220 | 	
221 | 	# "seed" initial population
222 | 	population[0][0] = 2
223 | 	population[0][1] = 4
224 | 	population[0][2] = 8
225 | 	population[0][3] = 4
226 | 	population[0][4] = 8
227 | 	population[0][5] = 1
228 | 	population[0][6] = 1
229 | 	population[0][7] = 1
230 | 	population[0][8] = 1
231 | 	population[0][9] = 2
232 | 	population[0][10] = 1
233 | 	population[0][11] = 2
234 | 	
235 | 	populationSpecialisation = numpy.random.randint(0, nSpecialisations, n, dtype=int)
236 | 	
237 | 	for x in range(n):
238 | 		p = multiprocessing.Process(
239 | 			target=__fitness_multiprocessing__, 
240 | 			args=(x, population[x], specialisationScenes[populationSpecialisation[x]], processReturn)
241 | 		)
242 | 		process.append(p)
243 | 		p.start()
244 | 		
245 | 		if x % batch_size == 0:
246 | 			for p in process:
247 | 				p.join()
248 | 	
249 | 	for p in process:
250 | 		p.join()
251 | 	
252 | 	populationFitness = numpy.array(processReturn[:n], dtype=float, copy=True, order=None, subok=False, ndmin=0)
253 | 	
254 | 	
255 | 	process = []
256 | 	
257 | 	return
258 | 
259 | 
260 | def __fitness__(candidate_solution: list, scene_file: str) -> (tuple, [float]):
261 | 	global parameters
262 | 	
263 | 	parameters = {
264 | 		"boidSize": 0.10922,
265 | 		"radii": {
266 | 			"separation": candidate_solution[0], 
267 | 			"alignment": candidate_solution[1], 
268 | 			"cohesion": candidate_solution[2], 
269 | 			"predator": candidate_solution[3], 
270 | 			"prey": candidate_solution[4]
271 | 		},
272 | 		"weights": {
273 | 			"separation": candidate_solution[5], 
274 | 			"alignment": candidate_solution[6], 
275 | 			"cohesion": candidate_solution[7], 
276 | 			"predator": candidate_solution[8], 
277 | 			"predatorBoost": candidate_solution[9], 
278 | 			"prey": candidate_solution[10], 
279 | 			"preyBoost": candidate_solution[11]
280 | 		},
281 | 		"maximumSpeed": 42  # candidate_solution[11]
282 | 	}
283 | 	
284 | 	return GeneticAlgorithmsForSwarmParameterTuning.__run__(parameters, scene_file)
285 | 
286 | 
287 | def __fitness_multiprocessing__(x: int, candidate_solution: list, scene_file: str, process_return: list):
288 | 	process_return[x] = __fitness__(candidate_solution, scene_file)[1]
289 | 	
290 | 	return
291 | 
292 | 
293 | def crossover(a: numpy.ndarray, b: numpy.ndarray) -> (numpy.ndarray, numpy.ndarray):
294 | 	size = min(len(a), len(b))
295 | 	
296 | 	if crossover_type < 1:
297 | 		lhs = numpy.random.randint(1 + 1, size=size) > 0
298 | 		rhs = lhs < 1
299 | 		
300 | 		children = (a * lhs + b * rhs, b * lhs + a * rhs)
301 | 	elif crossover_type == 1:
302 | 		start_point = random.randint(0, size)
303 | 		
304 | 		children = (
305 | 			numpy.concatenate((a[:start_point], b[start_point:])), numpy.concatenate((b[:start_point], a[start_point:]))
306 | 		)
307 | 	elif crossover_type == 2:
308 | 		start_point = random.randint(0, size)
309 | 		mid_point = random.randint(start_point, size)
310 | 		
311 | 		children = (
312 | 			numpy.concatenate((a[0:start_point], b[start_point:mid_point], a[mid_point:])), 
313 | 			numpy.concatenate((b[0:start_point], a[start_point:mid_point], b[mid_point:]))
314 | 		)
315 | 	else:
316 | 		return None
317 | 	
318 | 	return children
319 | 
320 | 
321 | def mutation(a: numpy.ndarray) -> numpy.ndarray:
322 | 	size = len(a)
323 | 	
324 | 	if mutation_type == 0:
325 | 		bit = numpy.random.rand(size) < (1 / size)
326 | 		
327 | 		child = (numpy.random.rand(size) * search_space) * (bit > 0) + a * (bit < 1)
328 | 	elif mutation_type == 1:
329 | 		child = numpy.array(search_space) - a
330 | 	elif mutation_type == 2:
331 | 		boundary = numpy.random.rand()
332 | 		
333 | 		if boundary < (1 / 3):
334 | 			child = numpy.clip(a, random.random() * search_space, None)  # lower bound
335 | 		elif boundary < (2 / 3):
336 | 			child = numpy.clip(a, None, random.random() * search_space)  # upper bound
337 | 		else:
338 | 			child = numpy.clip(a, random.random() * search_space, random.random() * search_space)  # lower and upper bound
339 | 	else:
340 | 		child = numpy.random.rand(size) * search_space
341 | 	
342 | 	return child
343 | 
344 | 
345 | if __name__ == "__main__":
346 | 	__main__()
347 | 


--------------------------------------------------------------------------------
/Genetic Algorithms for Swarm Parameter Tuning.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Cheejyg/Genetic-Algorithms-for-Swarm-Parameter-Tuning/a5ad093992ae7516d3bd08ff7c64da2971bdd428/Genetic Algorithms for Swarm Parameter Tuning.pdf


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 Cheejyg
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # Genetic Algorithms for Swarm Parameter Tuning
2 | Swarming behaviour is based on aggregation of simple drones exhibiting basic instinctive reactions to stimuli. However, to achieve overall balanced/interesting behaviour the relative importance of these instincts, as well their internal parameters, must be tuned. In this project, you will learn how to apply Genetic Programming as means of such tuning, and attempt to achieve a series of non-trivial swarm-level behaviours.
3 | 


--------------------------------------------------------------------------------
/parameters.json:
--------------------------------------------------------------------------------
 1 | {
 2 | 	"boidSize": 0.10922,
 3 | 	"radii": {
 4 | 		"separation": 2.0,
 5 | 		"alignment": 4.0,
 6 | 		"cohesion": 8.0,
 7 | 		"predator": 4.0,
 8 | 		"prey": 8.0
 9 | 	},
10 | 	"weights": {
11 | 		"separation": 1.0,
12 | 		"alignment": 1.0,
13 | 		"cohesion": 1.0,
14 | 		"predator": 1.0,
15 | 		"predatorBoost": 2.0,
16 | 		"prey": 1.0,
17 | 		"preyBoost": 2.0
18 | 	},
19 | 	"maximumSpeed": 5.4934993590917414392968320820363
20 | }


--------------------------------------------------------------------------------
/scene/parameters.csv:
--------------------------------------------------------------------------------
 1 | parameter,index
 2 | radii_separation,[0]
 3 | radii_alignment,[1]
 4 | radii_cohesion,[2]
 5 | radii_predator,[3]
 6 | radii_prey,[4]
 7 | weights_separation,[5]
 8 | weights_alignment,[6]
 9 | weights_cohesion,[7]
10 | weights_predator,[8]
11 | weights_predatorBoost,[9]
12 | weights_prey,[10]
13 | weights_preyBoost,[11]
14 | 


--------------------------------------------------------------------------------
/scene/scene.json:
--------------------------------------------------------------------------------
 1 | {
 2 | 	"window": {
 3 | 		"width": 100,
 4 | 		"height": 100,
 5 | 		"depth": null
 6 | 	},
 7 | 	"dimension": 2,
 8 | 	"ticks": 2048,
 9 | 	"boids": {
10 | 		"positions": [
11 | 			[0, 0],
12 | 			[0, 0],
13 | 			[0, 0],
14 | 			[0, 0]
15 | 		],
16 | 		"rotations": [
17 | 			[0, 0],
18 | 			[0, 0],
19 | 			[0, 0],
20 | 			[0, 0]
21 | 		],
22 | 		"velocities": [
23 | 			[0, 0],
24 | 			[0, 0],
25 | 			[0, 0],
26 | 			[0, 0]
27 | 		]
28 | 	},
29 | 	"predators": {
30 | 		"type": 1, 
31 | 		"positions": [
32 | 			[0, 0]
33 | 		],
34 | 		"rotations": [
35 | 			[0, 0]
36 | 		],
37 | 		"velocities": [
38 | 			[0, 0]
39 | 		]
40 | 	},
41 | 	"preys": {
42 | 		"type": 1, 
43 | 		"positions": [
44 | 			[0, 0]
45 | 		],
46 | 		"rotations": [
47 | 			[0, 0]
48 | 		],
49 | 		"velocities": [
50 | 			[0, 0]
51 | 		]
52 | 	}
53 | }


--------------------------------------------------------------------------------
/scene/scene_default.json:
--------------------------------------------------------------------------------
 1 | {
 2 | 	"window": {
 3 | 		"width": 100,
 4 | 		"height": 100,
 5 | 		"depth": null
 6 | 	},
 7 | 	"dimension": 2,
 8 | 	"ticks": 2048,
 9 | 	"boids": {
10 | 		"positions": [
11 | 			[0, 0],
12 | 			[0, 0],
13 | 			[0, 0],
14 | 			[0, 0],
15 | 			[0, 0],
16 | 			[0, 0],
17 | 			[0, 0],
18 | 			[0, 0]
19 | 		],
20 | 		"rotations": [
21 | 			[0, 0],
22 | 			[0, 0],
23 | 			[0, 0],
24 | 			[0, 0],
25 | 			[0, 0],
26 | 			[0, 0],
27 | 			[0, 0],
28 | 			[0, 0]
29 | 		],
30 | 		"velocities": [
31 | 			[0, 0],
32 | 			[0, 0],
33 | 			[0, 0],
34 | 			[0, 0],
35 | 			[0, 0],
36 | 			[0, 0],
37 | 			[0, 0],
38 | 			[0, 0]
39 | 		]
40 | 	},
41 | 	"predators": {
42 | 		"type": 2, 
43 | 		"positions": [
44 | 			[0, 0]
45 | 		],
46 | 		"rotations": [
47 | 			[0, 0]
48 | 		],
49 | 		"velocities": [
50 | 			[0, 0]
51 | 		], 
52 | 		"waypoints": [
53 | 			[[-75, 75], [75, -75]]
54 | 		]
55 | 	},
56 | 	"preys": {
57 | 		"type": 1, 
58 | 		"positions": [
59 | 			[0, 0], 
60 | 			[0, 0]
61 | 		],
62 | 		"rotations": [
63 | 			[0, 0], 
64 | 			[0, 0]
65 | 		],
66 | 		"velocities": [
67 | 			[0, 0], 
68 | 			[0, 0]
69 | 		]
70 | 	}
71 | }


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/scene/scene_fast.json:
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 1 | {
 2 | 	"window": {
 3 | 		"width": 100,
 4 | 		"height": 100,
 5 | 		"depth": null
 6 | 	},
 7 | 	"dimension": 2,
 8 | 	"ticks": 128,
 9 | 	"boids": {
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18 | 			[-8, 8],
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21 | 		"rotations": [
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32 | 		"velocities": [
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43 | 	},
44 | 	"predators": {
45 | 		"positions": [
46 | 			[-75, 75]
47 | 		],
48 | 		"rotations": [
49 | 			[0, 0]
50 | 		],
51 | 		"velocities": [
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53 | 		]
54 | 	},
55 | 	"preys": {
56 | 		"positions": [
57 | 			[-75, 75]
58 | 		],
59 | 		"rotations": [
60 | 			[0, 0]
61 | 		],
62 | 		"velocities": [
63 | 			[0, 0]
64 | 		]
65 | 	}
66 | }


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/scene/scenes.csv:
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1 | scene,n,predators (type),preys (type)
2 | scene_velocities.json,400,0,0
3 | scene_predators.json,400,4 (1),0
4 | scene_preys.json,400,0,4 (1)
5 | scene_distances.json,400,4 (1),4 (1)
6 | scene_distancesPredator.json,400,1 (2),1 (1)
7 | scene_distancesPrey.json,400,1 (2),1 (2)
8 | 


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