I need 2 floats, 1 bool and 1 int
@NirvanaDogra yes, plenty of people use GA to analyse data for the financial market
@jcbro not the best place to post code try the mailing list with gist or Pastebin
Hello everyone :) !
I am working on a problem that requires creating a function with n inputs and k outputs. From what I know a GP tree can have only one output, so I thought I would use a list of k trees each getting the same n inputs and producing one of the k outputs.
It seems like a good idea but I am hesitant if it is a correct approach. How will crossover and mutation operators work in case of a list of trees? Will that make sense? Maybe there is a better way?
Source code I have so far below:
I am working on a problem that requires creating a function with n inputs and k outputs. From what I know a GP tree can have only one output, so I thought I would use a list of k trees each getting the same n inputs and producing one of the k outputs.
It seems like a good idea but I am hesitant if it is a correct approach. How will crossover and mutation operators work in case of a list of trees? Will that make sense? Maybe there is a better way?
Source code I have so far below:
pset = gp.PrimitiveSet("MAIN", arity=n, prefix='x')
pset.addPrimitive(operators.addition, 2)
pset.addPrimitive(operators.subtract, 2)
pset.addPrimitive(operators.multiply, 2)
# and many more...
# One optimization objective.
creator.create("FitnessMin", base.Fitness, weights=(-1.0,))
# Arithmetic expression.
creator.create("Tree", gp.PrimitiveTree, pset=pset)
# Individual is a list consisting of n_components trees.
creator.create("Individual", list, fitness=creator.FitnessMin)
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=MIN_TREE_HEIGHT, max_=MAX_TREE_HEIGHT)
toolbox.register("tree", tools.initIterate, creator.Tree, toolbox.expr)
toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.tree, n=pop_size)
# Population.
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
Hi, I am working with genetic algorithm in DEAP. I want to parallelize the evalution part that's why I am using multiprocessing. The problem is evaluation function works for only one individual and after that it throws an error above ( Can't pickle <function evaluation at 0x000001ED70DA28C8>: it's not the same object as ga_evaluation.evaluation). How can I solve this issue, any ideas?
@buseoz_gitlab That is a function pickling problem. Functions are tricky to pickle, but from you error message here is what I would try first : https://stackoverflow.com/questions/1412787/picklingerror-cant-pickle-class-decimal-decimal-its-not-the-same-object
Hello, I'm new with EAP, but I have got a very complex individual (as a class), My class has got a mutate and combine method and a method which return two fitness values which should be maximized. In my case the constructure of the class need some initializing parameters. I don't find a tutorial which explain to use the Toolbox with a individual class. Can anybody help me?
I need only the "population creation" and the "individual selection" calls from the toolbox, I would like to use the SwarmParticle System https://deap.readthedocs.io/en/master/examples/pso_basic.html because I have got 2 maximazing parameters
In my case I need an own generator which returns a list of my class individuals, an own mutate & combine function and an own evaluate function
How can I do this?
@FranciscoPalomares use the feasibility function as in the constraint tutorial.
def feasible(individual):
"""Feasability function for the individual. Returns True if feasible False
otherwise."""
if int(individual[2]) < int(individual[1]):
return True
return False
toolbox.register("evaluate", evalOneMax)
toolbox.decorate("evaluate", tools.DeltaPenality(feasible,7.0))@FranciscoPalomares Documentation for DeltaPenalty and tutorial . 7.0 is the penalty applied to invalid individuals. It is a required argument.
Diversity strongly depends on your response domain. If your population converges too fast you can try to play with crossover and mutation parameters. Moreover having selection with randomness (like tournament with a low number of participant) may help.
Hello, thanks for this wonderful tool!
I have two parameters that I want to maximize with some fitness function. But each parameter has a certain limit to its range, i.e. parameter value is limit between -1 to 100, and parameter b range limit is between -50 to -10.
How can I limit the evolution of values to a certain range?
Thanks to the helpers :-)
I have two parameters that I want to maximize with some fitness function. But each parameter has a certain limit to its range, i.e. parameter value is limit between -1 to 100, and parameter b range limit is between -50 to -10.
How can I limit the evolution of values to a certain range?
Thanks to the helpers :-)
@Hananel-Hazan Hey, you can either use soft constraints, operators that handle bounds (bounded operators), or repair in your evaluation function.
@nbro I suggest you try to run the first few lines of the symbreg and generate individuals one at a time to see what they look like.
You'll get a sense of how it works. From there you can move to ant (which is a lot more complex)
Hi ! Thank you for this great tool. In my case, the evaluate function includeson a neural network and each individual is the weights of the network. for each indivual i perform model.predict(individual). It works fine but when I can't find how to make it work using multiprocessing. Is there someone who already did this and has an example on how to do it ? thanks.
Hi everyOne, I am new to DEAP library. I am trying to make an independent evolutionary core that works with the external app. This is a process I want to have:
1.Population variables and their fitness values production in an external app
2.passing the Population and fitness to the DEAP core to do the selection, mutation, and mating process and making a new offspring.
3.passing the offsprings to the external app and, as a response getting the fitness value.
And doing this process in a loop to reach the final goal. The problem that I have is that I don't, know how can I create a population from defined individual values. In all examples, POPULATIONS are created from random functions. Is there any way to generate a population from a defined set of variables and defining the fitness attribute for them?
workflow, download tar.gz from pypi and check the file, vs install via pip and browse to sitepackages and check the file
Hi, I am using deap library to apply genetic algorithm (GA) in a problem. In particular generations of GA (for example, gen =5 and 10) I want to add a number of individuals (suppose 3 individuals) to the population and remove the same number of other individuals (again 3 individuals but different from the added individuals) from the population. That is, I want to manually import several individuals and delete several other individuals and keep the size of population constant. The type of individuals is list of lists and the population is a list of individuals. When I do the above action, I get the error: "AttributeError: 'list' object has no attribute 'fitness'." I have checked the population before and after the above action several times and the characteristics of population are completely the same before and after changing its individuals. That is, the population before and after changing is a list of individuals and individuals are list of lists. But, if I don't apply the above action the GA is implemented without any error however after changing the individuals of the population I get the error. Could you please help me to resolve this problem?
2 replies
