# Calibrating a NetLogo model using Genetic Algorithms

This example presents step by step how to explore a NetLogo model with an Evolutionary/Genetic Algorithm (EA/GA) in OpenMOLE.
For more generic details regarding the exploitation of Genetic Algorithms using OpenMOLE, you can check the GA section of the methods documentation.

# The ant model

We demonstrate this tutorial using the ants foraging model present in the Netlogo library. This model was created by Ury Wilensky. According to NetLogo's website, this model is described as:
In this project, a colony of ants forages for food. Though each ant follows a set of simple rules, the colony as a whole acts in a sophisticated way. When an ant finds a piece of food, it carries the food back to the nest, dropping a chemical as it moves. When other ants “sniff” the chemical, they follow the chemical toward the food. As more ants carry food to the nest, they reinforce the chemical trail.

A visual representation of this model looks like that:

In this tutorial we use a headless version of the model. This modified version is available here.

# Define the problem to solve as an optimisation problem

This model manipulates three parameters:
• Population: number of Ants in the model,
• Evaporation-rate: controls the evaporation rate of the chemical,
• Diffusion-rate: controls the diffusion rate of the chemical.

Ants forage from three sources of food (see the number in the picture below). Each source is positioned at different distances from the Ant colony.

It can be interesting to search the best combination of the two parameters evaporation-rate and diffusion-rate which minimises the eating time of each food source.

To build our fitness function, we modify the NetLogo Ants source code to store for each food source the first ticks indicating that this food source is empty.

``````to compute-fitness
if ((sum [food] of patches with [food-source-number = 1] = 0) and (final-ticks-food1 = 0)) [
set final-ticks-food1 ticks ]
if ((sum [food] of patches with [food-source-number = 2] = 0) and (final-ticks-food2 = 0)) [
set final-ticks-food2 ticks ]
if ((sum [food] of patches with [food-source-number = 3] = 0) and (final-ticks-food3 = 0)) [
set final-ticks-food3 ticks ]
end``````

At the end of each simulation we return the values for the three objectives :
• The simulation ticks indicating that source 1 is empty,
• The simulation ticks indicating that source 2 is empty,
• The simulation ticks indicating that source 3 is empty.

The combination of the three objectives indicates the quality of the parameters used to run the simulation. This situation is a multi-objective optimisation problem. In case there is a compromise between these 3 objectives, we will obtain a Pareto frontier at the end of the optimisation process.

# Getting the ant model to run in OpenMOLE

When building a calibration or optimisation workflow, the first step is to make the model run in OpenMOLE. This script simply embeds the NetLogo model and runs one single execution of the model with arbitrary parameters. More details about the NetLogo5 task used in this script can be found in this section of the documentation.
``````// Define the input variables
val gPopulation = Val[Double]
val gDiffusionRate = Val[Double]
val gEvaporationRate = Val[Double]
val seed = Val[Int]

// Define the output variables
val food1 = Val[Double]
val food2 = Val[Double]
val food3 = Val[Double]

val cmds = Seq("run-to-grid")
val ants =
NetLogo5Task(workDirectory / "Ants.nlogo", cmds, seed = seed) set (
// Map the OpenMOLE variables to NetLogo variables
netLogoInputs += (gPopulation, "gpopulation"),
netLogoInputs += (gDiffusionRate, "gdiffusion-rate"),
netLogoInputs += (gEvaporationRate, "gevaporation-rate"),
netLogoOutputs += ("final-ticks-food1", food1),
netLogoOutputs += ("final-ticks-food2", food2),
netLogoOutputs += ("final-ticks-food3", food3),
// Define default values for inputs of the model
seed := 42,
gPopulation := 125.0,
gDiffusionRate := 50.0,
gEvaporationRate := 50
)

// Define the hooks to collect the results
val displayHook = ToStringHook(food1, food2, food3)

// Start a workflow with 1 task
val ex = (ants hook displayHook) start
``````

The result of this execution should look like:
`{food1=746.0, food2=1000.0, food3=2109.0}`

# The optimisation algorithm

We will try to find the parameter settings minimising these estimators. This script describes how to use the NSGA2 multi-objective optimisation algorithm in OpenMOLE. The result files are written to `/tmp/ants`.
``````// Define the population size: 100.
// Define the inputs and their respective variation bounds.
// Define the objectives to minimize.
// Tell OpenMOLE that this model is stochastic and that it should generate a seed for each execution
// Define the fitness evaluation
// Define the parallelism level
// Terminate after 10000 evaluations
val evolution =
// Define the population (10) and the number of generations (100).
// Define the inputs and their respective variation bounds.
// Define the objectives to minimize.
algorithm =
NSGA2(
mu = 100,
genome = Seq(gDiffusionRate in (0.0, 99.0), gEvaporationRate in (0.0, 99.0)),
objectives = Seq(food1, food2, food3),
stochastic = Stochastic(seed = seed)
),
evaluation = ants,
parallelism = 10,
termination = 1000
)

// Define a hook to save the Pareto frontier
val savePopulationHook = SavePopulationHook(evolution, workDirectory / "results")

// Plug everything together to create the workflow
(evolution hook savePopulationHook)``````

# Scale up

If you use distributed computing, it might be a good idea to opt for an Island model. Islands are better suited to exploit distributed computing resources than classical generational genetic algorithms. See how the end of the script changes to implement islands in the workflow. Here we compute 2,000 islands in parallel, each running during 1 hour on the European grid:
``````val evolution =
algorithm =
NSGA2(
mu = 100,
genome = Seq(gDiffusionRate in (0.0, 99.0), gEvaporationRate in (0.0, 99.0)),
objectives = Seq(food1, food2, food3),
stochastic = Stochastic(seed = seed)
),
evaluation = ants,
termination = 1 hour
)

// Define the island model with 1,000 concurrent islands. Each island gets 50 individuals sampled from the global
// population. The algorithm stops after 100,000 islands evaluations.
val island =
IslandEvolution(
evolution,
parallelism = 1000,
termination = 100000
)

val savePopulationHook = SavePopulationHook(island, workDirectory / "results")

// Define the execution environment
val env = EGIEnvironment("biomed", openMOLEMemory = 1200 megabytes, cpuTime = 4 hours)

// Define the workflow
(island on env hook savePopulationHook)``````