Issue Description
Hi DEAP team,
I'm a beginner using DEAP for genetic programming and have encountered an issue where setting a global random seed (random.seed(1)) does not produce consistent results across multiple runs. Specifically, the results for generation 0 are identical, but starting from generation 1, metrics like average fitness, standard deviation, and number of evaluations differ. I suspect the issue may be related to the selection or crossover processes, as modifying the selRandom function to use a local seed fixed the issue in my tests.
Thank you in advance for your help and for maintaining this amazing library!
Steps to Reproduce
- Set up a genetic programming experiment with the following configuration:
- Random seed:
random.seed(1), np.random.seed(1), torch.manual_seed(1)
- Population size: 1000
- Generations: 3
- Selection:
tools.selTournament with tournsize=20
- Crossover:
gp.cxOnePoint with probability 0.8
- Mutation:
gp.mutUniform with probability 0.3
- Run the experiment multiple times and compare the output metrics (e.g., average fitness, standard deviation, number of evaluations).
- Observe that generation 0 metrics are consistent, but subsequent generations differ.
Code Example
Here is the relevant code snippet:
import random
import numpy as np
import torch
from deap import base, creator, tools, gp
# Set random seed
random_state = 1
random.seed(random_state)
np.random.seed(random_state)
torch.manual_seed(random_state)
# Define problem-specific primitives (omitted for brevity)
# Assume pset is a gp.PrimitiveSetTyped object
# Set up DEAP
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", gp.PrimitiveTree, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("expr", gp.genHalfAndHalf, pset=pset, min_=1, max_=4)
toolbox.register("individual", tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("evaluate", calculate_fitness) # Custom fitness function (deterministic)
toolbox.register("mate", gp.cxOnePoint)
toolbox.register("expr_mut", gp.genFull, min_=0, max_=2)
toolbox.register("mutate", gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
toolbox.register("select", tools.selTournament, tournsize=20)
# Parameters
population_size = 1000
generations = 3
cxpb = 0.8
mutpb = 0.3
hall_of_fame_size = 10
# Run evolution (e.g., using eaSimple)
pop = toolbox.population(n=population_size)
hof = tools.HallOfFame(hall_of_fame_size)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register("avg", np.mean)
stats.register("std", np.std)
stats.register("min", np.min)
stats.register("max", np.max)
pop, logbook = algorithms.eaSimple(pop, toolbox, cxpb=cxpb, mutpb=mutpb, ngen=generations, stats=stats, halloffame=hof, verbose=True)
Issue Description
Hi DEAP team,
I'm a beginner using DEAP for genetic programming and have encountered an issue where setting a global random seed (
random.seed(1)) does not produce consistent results across multiple runs. Specifically, the results for generation 0 are identical, but starting from generation 1, metrics like average fitness, standard deviation, and number of evaluations differ. I suspect the issue may be related to the selection or crossover processes, as modifying theselRandomfunction to use a local seed fixed the issue in my tests.Thank you in advance for your help and for maintaining this amazing library!
Steps to Reproduce
random.seed(1),np.random.seed(1),torch.manual_seed(1)tools.selTournamentwithtournsize=20gp.cxOnePointwith probability 0.8gp.mutUniformwith probability 0.3Code Example
Here is the relevant code snippet: