blocks|key|1492848|text|DEAP:分布式进化算法同时支持Python2和3：http://code.google.com/p/deap|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1492849|免责声明:我是DEAP的开发者之一。|1492850|entityMap|0|LINK|mutability|MUTABLE|url|http://code.google.com/p/deap^0|Q|T|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@]|9|@$A|O|B|P|1|Q]]|C|$]]|$1|D|3|E|5|6|7|R|8|@]|9|@]|C|$]]|$1|F|3|-4|5|6|7|S|8|@]|9|@]|C|$]]]|G|$H|$5|I|J|K|C|$L|M]]]]

DEAP: Distributed Evolutionary Algorithms supports both Python 2 and 3:
<a href="http://code.google.com/p/deap" rel="noreferrer">http://code.google.com/p/deap</a>

Disclaimer : I am one of the developers of DEAP.

blocks|key|349752|text|虽然不完全是GA库，但克林顿·谢泼德的"Genetic+Algorithms+with+Python“一书非常有用，因为它可以帮助您根据自己的需要构建自己的GA库。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|349753|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Not exactly a GA library, but the book "Genetic Algorithms with Python" from Clinton Sheppard is quite useful as it helps you build your own GA library specified for your needs.

blocks|key|3459809|text|这是一个不需要组装的包，可以用于任何问题：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3459810|https://pypi.org/project/geneticalgorithm/|offset|length|3459811|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|16|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|N|8|@]|9|@$D|O|E|P|1|Q]]|A|$]]|$1|F|3|-4|5|6|7|R|8|@]|9|@]|A|$]]]|G|$H|$5|I|J|K|A|$L|C]]]]

This is a package that does not need assembly and can be used for any problem:

<a href="https://pypi.org/project/geneticalgorithm/" rel="nofollow noreferrer">https://pypi.org/project/geneticalgorithm/</a>

blocks|key|3459828|text|Check+PyGAD，一个开源的Python3库，用于实现遗传算法和训练机器学习算法。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|3459829|文档可在Read+The+Docs：https://pygad.readthedocs.io上找到|3459830|通过pip安装：pip+install+pygad|style|CODE|3459831|下面是一个使用PyGAD优化线性模型的示例。|3459832|import+pygad
import+numpy

"""
Given+the+following+function:
++++y+=+f(w1:w6)+=+w1x1+%2B+w2x2+%2B+w3x3+%2B+w4x4+%2B+w5x5+%2B+6wx6
++++where+(x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7)+and+y=44
What+are+the+best+values+for+the+6+weights+(w1+to+w6)?+We+are+going+to+use+the+genetic+algorithm+to+optimize+this+function.
"""

function_inputs+=+[4,-2,3.5,5,-11,-4.7]+#+Function+inputs.
desired_output+=+44+#+Function+output.

def+fitness_func(solution,+solution_idx):
++++#+Calculating+the+fitness+value+of+each+solution+in+the+current+population.
++++#+The+fitness+function+calulates+the+sum+of+products+between+each+input+and+its+corresponding+weight.
++++output+=+numpy.sum(solution*function_inputs)
++++fitness+=+1.0+/+numpy.abs(output+-+desired_output)
++++return+fitness

fitness_function+=+fitness_func

num_generations+=+100+#+Number+of+generations.
num_parents_mating+=+7+#+Number+of+solutions+to+be+selected+as+parents+in+the+mating+pool.

#+To+prepare+the+initial+population,+there+are+2+ways:
#+1)+Prepare+it+yourself+and+pass+it+to+the+initial_population+parameter.+This+way+is+useful+when+the+user+wants+to+start+the+genetic+algorithm+with+a+custom+initial+population.
#+2)+Assign+valid+integer+values+to+the+sol_per_pop+and+num_genes+parameters.+If+the+initial_population+parameter+exists,+then+the+sol_per_pop+and+num_genes+parameters+are+useless.
sol_per_pop+=+50+#+Number+of+solutions+in+the+population.
num_genes+=+len(function_inputs)

init_range_low+=+-2
init_range_high+=+5

parent_selection_type+=+"sss"+#+Type+of+parent+selection.
keep_parents+=+7+#+Number+of+parents+to+keep+in+the+next+population.+-1+means+keep+all+parents+and+0+means+keep+nothing.

crossover_type+=+"single_point"+#+Type+of+the+crossover+operator.

#+Parameters+of+the+mutation+operation.
mutation_type+=+"random"+#+Type+of+the+mutation+operator.
mutation_percent_genes+=+10+#+Percentage+of+genes+to+mutate.+This+parameter+has+no+action+if+the+parameter+mutation_num_genes+exists+or+when+mutation_type+is+None.

last_fitness+=+0
def+callback_generation(ga_instance):
++++global+last_fitness
++++print("Generation+=+{generation}".format(generation=ga_instance.generations_completed))
++++print("Fitness++++=+{fitness}".format(fitness=ga_instance.best_solution()[1]))
++++print("Change+++++=+{change}".format(change=ga_instance.best_solution()[1]+-+last_fitness))

#+Creating+an+instance+of+the+GA+class+inside+the+ga+module.+Some+parameters+are+initialized+within+the+constructor.
ga_instance+=+pygad.GA(num_generations=num_generations,
+++++++++++++++++++++++num_parents_mating=num_parents_mating,+
+++++++++++++++++++++++fitness_func=fitness_function,
+++++++++++++++++++++++sol_per_pop=sol_per_pop,+
+++++++++++++++++++++++num_genes=num_genes,
+++++++++++++++++++++++init_range_low=init_range_low,
+++++++++++++++++++++++init_range_high=init_range_high,
+++++++++++++++++++++++parent_selection_type=parent_selection_type,
+++++++++++++++++++++++keep_parents=keep_parents,
+++++++++++++++++++++++crossover_type=crossover_type,
+++++++++++++++++++++++mutation_type=mutation_type,
+++++++++++++++++++++++mutation_percent_genes=mutation_percent_genes,
+++++++++++++++++++++++callback_generation=callback_generation)

#+Running+the+GA+to+optimize+the+parameters+of+the+function.
ga_instance.run()

#+After+the+generations+complete,+some+plots+are+showed+that+summarize+the+how+the+outputs/fitenss+values+evolve+over+generations.
ga_instance.plot_result()

#+Returning+the+details+of+the+best+solution.
solution,+solution_fitness,+solution_idx+=+ga_instance.best_solution()
print("Parameters+of+the+best+solution+:+{solution}".format(solution=solution))
print("Fitness+value+of+the+best+solution+=+{solution_fitness}".format(solution_fitness=solution_fitness))
print("Index+of+the+best+solution+:+{solution_idx}".format(solution_idx=solution_idx))

prediction+=+numpy.sum(numpy.array(function_inputs)*solution)
print("Predicted+output+based+on+the+best+solution+:+{prediction}".format(prediction=prediction))

if+ga_instance.best_solution_generation+!=+-1:
++++print("Best+fitness+value+reached+after+{best_solution_generation}+generations.".format(best_solution_generation=ga_instance.best_solution_generation))

#+Saving+the+GA+instance.
filename+=+'genetic'+#+The+filename+to+which+the+instance+is+saved.+The+name+is+without+extension.
ga_instance.save(filename=filename)

#+Loading+the+saved+GA+instance.
loaded_ga_instance+=+pygad.load(filename=filename)
loaded_ga_instance.plot_result()|code-block|syntax|javascript|3459833|entityMap|0|LINK|mutability|MUTABLE|url|https://pygad.readthedocs.io/|1^0|6|5|0|0|I|S|1|0|8|H|0|0|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|D|3|E|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|F|3|G|5|6|7|17|8|@$A|18|B|19|H|I]]|9|@]|C|$]]|$1|J|3|K|5|6|7|1A|8|@]|9|@]|C|$]]|$1|L|3|M|5|N|7|1B|8|@]|9|@]|C|$O|P]]|$1|Q|3|-4|5|6|7|1C|8|@]|9|@]|C|$]]]|R|$S|$5|T|U|V|C|$W|X]]|Y|$5|T|U|V|C|$W|X]]]]

Check <a href="https://pygad.readthedocs.io" rel="nofollow noreferrer">PyGAD</a>, an open-source Python 3 library for implementing the genetic algorithm and training machine learning algorithms.
The documentation is available at Read the Docs: <a href="https://pygad.readthedocs.io" rel="nofollow noreferrer">https://pygad.readthedocs.io</a>
Install it via pip: <code>pip install pygad</code>
Here is an example that uses PyGAD to optimize a linear model.
<pre class="lang-py prettyprint-override"><code>import pygad
import numpy

&quot;&quot;&quot;
Given the following function:
 y = f(w1:w6) = w1x1 + w2x2 + w3x3 + w4x4 + w5x5 + 6wx6
 where (x1,x2,x3,x4,x5,x6)=(4,-2,3.5,5,-11,-4.7) and y=44
What are the best values for the 6 weights (w1 to w6)? We are going to use the genetic algorithm to optimize this function.
&quot;&quot;&quot;

function_inputs = [4,-2,3.5,5,-11,-4.7] # Function inputs.
desired_output = 44 # Function output.

def fitness_func(solution, solution_idx):
 # Calculating the fitness value of each solution in the current population.
 # The fitness function calulates the sum of products between each input and its corresponding weight.
 output = numpy.sum(solution*function_inputs)
 fitness = 1.0 / numpy.abs(output - desired_output)
 return fitness

fitness_function = fitness_func

num_generations = 100 # Number of generations.
num_parents_mating = 7 # Number of solutions to be selected as parents in the mating pool.

# To prepare the initial population, there are 2 ways:
# 1) Prepare it yourself and pass it to the initial_population parameter. This way is useful when the user wants to start the genetic algorithm with a custom initial population.
# 2) Assign valid integer values to the sol_per_pop and num_genes parameters. If the initial_population parameter exists, then the sol_per_pop and num_genes parameters are useless.
sol_per_pop = 50 # Number of solutions in the population.
num_genes = len(function_inputs)

init_range_low = -2
init_range_high = 5

parent_selection_type = &quot;sss&quot; # Type of parent selection.
keep_parents = 7 # Number of parents to keep in the next population. -1 means keep all parents and 0 means keep nothing.

crossover_type = &quot;single_point&quot; # Type of the crossover operator.

# Parameters of the mutation operation.
mutation_type = &quot;random&quot; # Type of the mutation operator.
mutation_percent_genes = 10 # Percentage of genes to mutate. This parameter has no action if the parameter mutation_num_genes exists or when mutation_type is None.

last_fitness = 0
def callback_generation(ga_instance):
 global last_fitness
 print(&quot;Generation = {generation}&quot;.format(generation=ga_instance.generations_completed))
 print(&quot;Fitness = {fitness}&quot;.format(fitness=ga_instance.best_solution()[1]))
 print(&quot;Change = {change}&quot;.format(change=ga_instance.best_solution()[1] - last_fitness))

# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
ga_instance = pygad.GA(num_generations=num_generations,
 num_parents_mating=num_parents_mating, 
 fitness_func=fitness_function,
 sol_per_pop=sol_per_pop, 
 num_genes=num_genes,
 init_range_low=init_range_low,
 init_range_high=init_range_high,
 parent_selection_type=parent_selection_type,
 keep_parents=keep_parents,
 crossover_type=crossover_type,
 mutation_type=mutation_type,
 mutation_percent_genes=mutation_percent_genes,
 callback_generation=callback_generation)

# Running the GA to optimize the parameters of the function.
ga_instance.run()

# After the generations complete, some plots are showed that summarize the how the outputs/fitenss values evolve over generations.
ga_instance.plot_result()

# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
print(&quot;Parameters of the best solution : {solution}&quot;.format(solution=solution))
print(&quot;Fitness value of the best solution = {solution_fitness}&quot;.format(solution_fitness=solution_fitness))
print(&quot;Index of the best solution : {solution_idx}&quot;.format(solution_idx=solution_idx))

prediction = numpy.sum(numpy.array(function_inputs)*solution)
print(&quot;Predicted output based on the best solution : {prediction}&quot;.format(prediction=prediction))

if ga_instance.best_solution_generation != -1:
 print(&quot;Best fitness value reached after {best_solution_generation} generations.&quot;.format(best_solution_generation=ga_instance.best_solution_generation))

# Saving the GA instance.
filename = 'genetic' # The filename to which the instance is saved. The name is without extension.
ga_instance.save(filename=filename)

# Loading the saved GA instance.
loaded_ga_instance = pygad.load(filename=filename)
loaded_ga_instance.plot_result()
</code></pre>

blocks|key|1334589|text|嘿，这基本上是一个插头，但我想你们会喜欢的！|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1334590|​|1334591|📷|atomic|offset|length|1334592|1334593|EasyGA是一个python包，旨在提供易于使用的遗传算法。该软件包设计为开箱即用，同时还允许您按自己认为合适的方式自定义功能。|1334594|这里是wiki，它最好地解释了流程是如何工作的。https://github.com/danielwilczak101/EasyGA/wiki|1334595|这就是让它工作所需的一切：|style|BOLD|1334596|pip3+install+EasyGA|code-block|syntax|javascript|1334597|和一些示例代码：|1334598|import+EasyGA

#+Create+the+Genetic+algorithm
ga+=+EasyGA.GA()

#+Evolve+the+genetic+algorithm
ga.evolve()

#+Print+your+default+genetic+algorithm
ga.print_generation()
ga.print_population()|1334599|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://ask.qcloudimg.com/http-save/yehe-900000/03db97abe7ab4fb660a7766269fb5dd7.png|imageAlt|1|LINK|MUTABLE|url|https://github.com/danielwilczak101/EasyGA/wiki^0|0|0|0|1|0|0|0|0|O|1B|1|0|0|D|0|0|0|8|0|0^^$0|@$1|2|3|4|5|6|7|1E|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1F|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1G|8|@]|9|@$G|1H|H|1I|1|1J]]|A|$]]|$1|I|3|C|5|6|7|1K|8|@]|9|@]|A|$]]|$1|J|3|K|5|6|7|1L|8|@]|9|@]|A|$]]|$1|L|3|M|5|6|7|1M|8|@]|9|@$G|1N|H|1O|1|1P]]|A|$]]|$1|N|3|O|5|6|7|1Q|8|@$G|1R|H|1S|P|Q]]|9|@]|A|$]]|$1|R|3|S|5|T|7|1T|8|@]|9|@]|A|$U|V]]|$1|W|3|X|5|6|7|1U|8|@$G|1V|H|1W|P|Q]]|9|@]|A|$]]|$1|Y|3|Z|5|T|7|1X|8|@]|9|@]|A|$U|V]]|$1|10|3|-4|5|6|7|1Y|8|@]|9|@]|A|$]]]|11|$12|$5|13|14|15|A|$16|17|18|-4]]|19|$5|1A|14|1B|A|$1C|1D]]]]

Hey this is basically a plug but I think you guys will like it!
<a href="https://i.stack.imgur.com/08Y4h.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/08Y4h.png" alt="EasyGA logo" /></a>
EasyGA is a python package designed to provide an easy-to-use Genetic Algorithm. The package is designed to work right out of the box, while also allowing you to customize features as you see fit.
Here is the wiki, it does the best at explaining how the flow works.
<a href="https://github.com/danielwilczak101/EasyGA/wiki" rel="nofollow noreferrer">https://github.com/danielwilczak101/EasyGA/wiki</a>
This is all you need to get it working:
<pre><code>pip3 install EasyGA
</code></pre>
and some sample code:
<pre><code>import EasyGA

# Create the Genetic algorithm
ga = EasyGA.GA()

# Evolve the genetic algorithm
ga.evolve()

# Print your default genetic algorithm
ga.print_generation()
ga.print_population()
</code></pre>

blocks|key|3459996|text|scikit选项|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3459997|https://github.com/guofei9987/scikit-opt|offset|length|3459998|import+numpy+as+np


def+schaffer(p):
++++'''
++++This+function+has+plenty+of+local+minimum,+with+strong+shocks
++++global+minimum+at+(0,0)+with+value+0
++++https://en.wikipedia.org/wiki/Test_functions_for_optimization
++++'''
++++x1,+x2+=+p
++++part1+=+np.square(x1)+-+np.square(x2)
++++part2+=+np.square(x1)+%2B+np.square(x2)
++++return+0.5+%2B+(np.square(np.sin(part1))+-+0.5)+/+np.square(1+%2B+0.001+*+part2)|code-block|syntax|javascript|3459999|Step2:做遗传算法|3460000|from+sko.GA+import+GA

ga+=+GA(func=schaffer,+n_dim=2,+size_pop=50,+max_iter=800,+prob_mut=0.001,+lb=[-1,+-1],+ub=[1,+1],+precision=1e-7)
best_x,+best_y+=+ga.run()
print('best_x:',+best_x,+'\n',+'best_y:',+best_y)|3460001|enter+image+description+here|3460002|entityMap|0|LINK|mutability|MUTABLE|url|1|https://i.stack.imgur.com/mAucy.png^0|0|0|14|0|0|0|0|0|0|S|1|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|10|8|@]|9|@$D|11|E|12|1|13]]|A|$]]|$1|F|3|G|5|H|7|14|8|@]|9|@]|A|$I|J]]|$1|K|3|L|5|6|7|15|8|@]|9|@]|A|$]]|$1|M|3|N|5|H|7|16|8|@]|9|@]|A|$I|J]]|$1|O|3|P|5|6|7|17|8|@]|9|@$D|18|E|19|1|1A]]|A|$]]|$1|Q|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|R|$S|$5|T|U|V|A|$W|C]]|X|$5|T|U|V|A|$W|Y]]]]

scikit-opt
<a href="https://github.com/guofei9987/scikit-opt" rel="nofollow noreferrer">https://github.com/guofei9987/scikit-opt</a>
<pre><code>import numpy as np


def schaffer(p):
 '''
 This function has plenty of local minimum, with strong shocks
 global minimum at (0,0) with value 0
 https://en.wikipedia.org/wiki/Test_functions_for_optimization
 '''
 x1, x2 = p
 part1 = np.square(x1) - np.square(x2)
 part2 = np.square(x1) + np.square(x2)
 return 0.5 + (np.square(np.sin(part1)) - 0.5) / np.square(1 + 0.001 * part2)
</code></pre>
Step2: do Genetic Algorithm
<pre><code>from sko.GA import GA

ga = GA(func=schaffer, n_dim=2, size_pop=50, max_iter=800, prob_mut=0.001, lb=[-1, -1], ub=[1, 1], precision=1e-7)
best_x, best_y = ga.run()
print('best_x:', best_x, '\n', 'best_y:', best_y)
</code></pre>
<a href="https://i.stack.imgur.com/mAucy.png" rel="nofollow noreferrer">enter image description here</a>

I'm currently looking for a mature GA library for python 3.x. But the only GA library can be found are <code>pyevolve</code> and <code>pygene</code>. They both support python 2.x only. I'd appreciate if anyone could help.

Any Genetic Algorithms module for python 3.x?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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我目前正在寻找一个用于python3.x的成熟GA库。但是唯一可以找到的GA库是pyevolve和pygene。它们都只支持python 2.x。如果有人能帮上忙，我将不胜感激。

问有没有适用于python 3.x的遗传算法模块？
EN

回答 6

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问有没有适用于python 3.x的遗传算法模块？EN

回答 6

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问有没有适用于python 3.x的遗传算法模块？
EN