11.4: Simulation

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Now that we have agents and a fitness landscape, I’ll define a class called Simulation that simulates the creation, reproduction, and death of the agents. To avoid getting bogged down, I’ll present a simplified version of the code here; you can see the details in the notebook for this chapter.

Here’s the definition of Simulation:

class Simulation: 
    
    def __init__(self, fit_land, agents): 
        self.fit_land = fit_land 
        self.agents = agents

The attributes of a Simulation are:

fit_land: A reference to a FitnessLandscape object.
agents: An array of Agent objects.

The most important function in Simulation is step, which simulates one time step:

# class Simulation: 

    def step(self): 
        n = len(self.agents) 
        fits = self.get_fitnesses()
        
        # see who dies 
        index_dead = self.choose_dead(fits) 
        num_dead = len(index_dead) 
         
        # replace the dead with copies of the living 
        replacements = self.choose_replacements(num_dead, fits) 
        self.agents[index_dead] = replacements

step uses three other methods:

get_fitnesses returns an array containing the fitness of each agent.
choose_dead decides which agents die during this time step, and returns an array that contains the indices of the dead agents.
choose_replacements decides which agents reproduce during this time step, invokes copy on each one, and returns an array of new Agent objects.

In this version of the simulation, the number of new agents during each time step equals the number of dead agents, so the number of live agents is constant.