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+# Fitness function and simulation
+* Revisiting the GA, what the process looks like and where we're at in that process.
+* Random population, how to generate one.
+* Running in simulation, how we run all the individuals in a population inside a simulation.
+---
+### Measuring distance moved
+Implement a fitness function to evaluate how far they have moved.
+
+### Generating a random population
+We'll create multiple random genemes and wrap them in a creature class.
+
+### Running in simulation
+* We'll create a new class for the Simulation.
+* Set up a pybullet environment in DIRECT mode, not using the GUI, which runs faster.
+* Create (intantiate) an individual inside the simulation (phenome) as we have been doing before
+* Step through time, running the simulation as quickly as possible and update the motors as we go so the creature moves as it should.
+* End the simulation
+
+### Measuring distance moved
+* Position at the start of simulation
+* Position at the end of simulation
+
+## Running creatures in simulation
+* New module
+* run_create funciton
+* update_motors function
+* Updating creature position
+
+### Build the simulation class
+* new module
+* set up a pybullet environment in DIRECT mode
+* take account of physicsClientId in all pybullet calls
+* runCreature
+* multi-process version
+
+### run_creature
+* resetSimulation
+* setGravity
+* write creature to XML and load it back in
+* stepSimulation
+* after steps iterations, exit
+* Creature.update_position
+
+## Implement the fitness function
+### What to measure?
+We'll just use distance travelled.
+### Compute distance travelled
+The euclidian distance between a and b.
+
+`np.linalg.norm(a-b)`
+
+### Evaluate population
+Test to evaluate a population of random creatures
+
+### The Flying-into-the-air problem
+Some creatures get an unfair advantage when they are crated with the floor inside. We set the position to [0, 0, 3], 3 units above ground
+
+### Other things we might consider
+* Energy use
+* Competing with other creatures
+
+## Multi-process evaluation
+### Motivation
+Running a GA is slow. Let's make it multi-processor capable
+
+Steps:
+1. Prepare the simulation class by making the urdf file be crated dynamically
+```python
+ xml_file = 'temp' + str(self.sim_id) + '.urdf'
+```
+2. create a ThreadedSim class
+* Constructor creates multiple Simulations with ids
+* Statis function to run a creature in a simulation and return the result
+
+```python
+class ThreadedSim():
+    def __init__(self, pool_size):
+        self.sims = [Simulation(i) for i in range(pool_size)]
+
+    @staticmethod
+    def static_run_creature(sim, cr, iterations):
+        sim.run_creature(cr, iterations)
+        return cr
+```
+3. Prepare the pool arguments
+* A pool takes a list of lists of args, one for each process in the pool<br>`[[arg1, arg2, arg3], [arg1, arg2, arg3]]`
+* As pool size (CPU count) is smaller than pop size, we will need multiple sets of pools<br>`[ [[arg1, arg2, arg3], [...]], [[arg1, arg2, arg3], [...]] ]`
+4. Create the pools and run the creatures
+* Now iterate over the sets of pool args and create one process pool for each
+
+Summary
+* Motivation
+* Prepare the sim
+* ThreadedSim class
+* Static run creature
+* Pool args
+
+## Select parents
+We'll select the parents u sing roulette wheel selection.
+
+## Crossover between parents
+We'll do single point crossover. It's possible to do multi-point crossover but we won't implement that.
+
+![](Files/image1.png)
+
+## Mutation
+• Mutate a single number
+• Add a gene
+• Remove a gene
+