human
I am working on the file "EvolAlgoCalibration.ipynb".
The first task is:
Problem
We try to match average simulation stylized facts as closely as possible to observed stylized facts. 
For that, we use an evolutionary algorithm to minimize a cost function. 
Create population of individuals
In our algorithm, an individual is a set a of parameters and its average associated values for stylized facts over several simulation runs.
Can you write Python program for it?

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Write me a the Python program you requested?
assistant
#Here is the Python program you requested.

class Individual:
    """The order class can represent both bid or ask type orders"""
    def __init__(self, parameters, stylized_facts, cost):
        self.parameters = parameters
        self.stylized_facts = stylized_facts
        self.cost = cost

    def __lt__(self, other):
        """Allows comparison to other individuals based on its cost (negative fitness)"""
        return self.cost < other.cost

# create initial population
population = []
for parameters in latin_hyper_cube:
    # add an individual to the population
    population.append(Individual(parameters, [], np.inf))
# create populations_over_time
populations_over_time = [population]