Get Started - Tutorial

Pyevolve combined with Python language can be a powerful tool. The best way to show you how Pyevolve can be used, is beginning with simple examples, later we’ll show some snippets and etc. So you’ll can walk by yourself.

First Example

To make the API easy, we have created default parameters for almost every parameter in Pyevolve, for example, when you will use the G1DList.G1DList genome without specifying the Mutator, Crossover and Initializator, you will use the default ones: Swap Mutator, One Point Crossover and the Integer Initialzator. All those default parameters are specified in the Consts module (and you are highly encouraged to take a look at source code).

Let’s begin with the first simple example (Ex. 1). First of all, you must know your problem, in this case, our problem is to find a simple 1D list of integers of n-size with zero in all positions. At the first look, we know by intuition that the representation needed to this problem is a 1D List, which you can found in Pyevolve by the name of G1DList.G1DList, which means Genome 1D List. This representation is based on a python list as you will see, and is very easy to manipulate. The next step is to define the our evaluation function to our Genetic Algorithm. We want all the n list positions with value of ‘0’, so we can propose the evaluation function:

As you can see in the above equation, with the x variable representing our genome list of integers, the f(x) shows our evaluation function, which is the sum of ‘0’ values in the list. For example, if we have a list with 10 elements like this:

x = [1, 2, 3, 8, 0, 2, 0, 4, 1, 0]

we will got the raw score [1] value of 3, or f(x) = 3. It’s very simple to understand. Now, let’s code this.

We will define our evaluation function “eval_func” as:

# This function is the evaluation function, we want
# to give high score to more zero'ed chromosomes
def eval_func(chromosome):
   score = 0.0

   # iterate over the chromosome elements (items)
   for value in chromosome:
      if value==0:
         score += 1.0

   return score

As you can see, this evaluation function verify each element in the list which is equal to ‘0’ and return the proportional score value. The G1DList.G1DList chromosome is not a python list by itself but it encapsulates one and exposes the methods for this list, like the iterator used in the above loop. The next step is the creation of an one sample genome [2] for the Genetic Algorithm. We can define our genome as this:

# Genome instance
genome = G1DList.G1DList(20)

# The evaluator function (objective function)
genome.evaluator.set(eval_func)

This will create an instance of the G1DList.G1DList class (which resides in the G1DList module) with the list n-size of 20 and sets the evaluation function of the genome to the evaluation function “eval_func” that we have created before.

But wait, where is the range of integers that will be used in the list ? Where is the mutator, crossover and initialization functions ? They are all in the default parameters, as you see, this parameters keep things simple.

By default (and you have the documentation to find this defaults), the range of the integers in the G1DList.G1DList is between the inverval [ Consts.CDefRangeMin, Consts.CDefRangeMax] inclusive, and genetic operators is the same I have cited before: Swap Mutator Mutators.G1DListMutatorSwap(), One Point Crossover Crossovers.G1DListCrossoverSinglePoint() and the Integer Initializator Initializators.G1DListInitializatorInteger(). You can change everything with the API, for example, you can pass the ranges to the genome, like this:

genome.setParams(rangemin=0, rangemax=10)

Right, now we have our evaluation function and our first genome ready, the next step is to create our Genetic Algorithm Engine, the GA Core which will do the evolution, control statistics, etc... The GA Engine which we will use is the GSimpleGA.GSimpleGA which resides in the GSimpleGA module, this GA Engine is the genetic algorithm [3] described by Goldberg. So, let’s create the engine:

ga = GSimpleGA.GSimpleGA(genome)

Ready ! Simple not ? We simple create our GA Engine with the created genome. You can ask: “Where is the selector method ? The number of generations ? Mutation rate ?“. Again: we have defaults. By default, the GA will evolve for 100 generations with a population size of 80 individuals, it will use the mutation rate of 2% and a crossover rate of 80%, the default selector is the Ranking Selection (Selectors.GRankSelector()) method. Those default parameters was not random picked, they are all based on the commom used properties.

Now, all we need to do is to evolve !

# Do the evolution, with stats dump
# frequency of 10 generations
ga.evolve(freq_stats=10)

# Best individual
print ga.bestIndividual()

Note

Pyevolve have the __repr__() function implemented for almost all objects, this means that you can use syntax like ‘print object’ and the object information will be show in an pretty format.

Ready, now we have our first Genetic Algorithm, it looks more like a “Hello GA !” application. The code above shows the call of the GSimpleGA.GSimpleGA.evolve() method, with the parameter freq_stats=10, this method will do the evolution and will show the statistics every 10th generation; the next method called is the GSimpleGA.GSimpleGA.bestIndividual(), this method will return the best individual after the end of the evolution, and the with the print python command, we will show the genome on the screen.

This is what this example will results:

Gen. 1 (1.00%): Max/Min/Avg Fitness(Raw)     [2.40(3.00)   / 1.60(1.00)   /   2.00(2.00)]
Gen. 10 (10.00%): Max/Min/Avg Fitness(Raw)   [10.80(10.00) / 7.20(8.00)   /   9.00(9.00)]
Gen. 20 (20.00%): Max/Min/Avg Fitness(Raw)   [22.80(20.00) / 15.20(18.00) / 19.00(19.00)]
Gen. 30 (30.00%): Max/Min/Avg Fitness(Raw)   [20.00(20.00) / 20.00(20.00) / 20.00(20.00)]
                 (...)
Gen. 100 (100.00%): Max/Min/Avg Fitness(Raw) [20.00(20.00) / 20.00(20.00) / 20.00(20.00)]

Total time elapsed: 3.375 seconds.

- GenomeBase
        Score:                   20.000000
        Fitness:                 20.000000

        Slot [Evaluator] (Count: 1)
                Name: eval_func
        Slot [Initializator] (Count: 1)
                Name: G1DListInitializatorInteger
                Doc:  Integer initialization function of G1DList,
                      accepts 'rangemin' and 'rangemax'
        Slot [Mutator] (Count: 1)
                Name: G1DListMutatorSwap
                Doc:  The mutator of G1DList, Swap Mutator
        Slot [Crossover] (Count: 1)
                Name: G1DListCrossoverSinglePoint
                Doc:  The crossover of G1DList, Single Point
- G1DList
        List size:       20
        List:            [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

This is the evolution of our Genetic Algorithm with the best individual show at the end of the evolution. As you can see, the population have obtained the best raw score (20.00) near the generation 20.

Final source code

Here is the final source code:

from pyevolve import G1DList
from pyevolve import GSimpleGA

def eval_func(chromosome):
   score = 0.0
   # iterate over the chromosome
   for value in chromosome:
      if value==0:
         score += 1
   return score

genome = G1DList.G1DList(20)
genome.evaluator.set(eval_func)
ga = GSimpleGA.GSimpleGA(genome)
ga.evolve(freq_stats=10)
print ga.bestIndividual()

Footnotes

[1]	It is important to note that in Pyevolve, we have raw score and fitness score, the raw score is the return of the evaluation function and the fitness score is the scaled score.

[2]	The term sample genome means one genome which provides the main configuration for all individuals.

[3]	This GA uses non-overlapping populations.

The Interactive Mode

Pyevolve have introduced the concept of the Interactive Mode in the course of evolution. When you are evolving, and the Interactive Mode is enabled, you can press the ESC Key anytime in the evolution process. By pressing that key, you will enter in the interactive mode, with a normal python prompt and the Interaction module exposed to you as the “it” module.

If you want to continue the evolution, just press CTRL-D on Linux or CTRL-Z on Windows.

See this session example:

# pyevolve_ex1_simple.py
Gen. 1 (0.20%): Max/Min/Avg Fitness(Raw) [6.18(11.00)/4.42(1.00)/5.15(5.15)]
Gen. 20 (4.00%): Max/Min/Avg Fitness(Raw) [11.70(15.00)/7.24(3.00)/9.75(9.75)]
Gen. 40 (8.00%): Max/Min/Avg Fitness(Raw) [17.99(21.00)/12.00(9.00)/14.99(14.99)]
Loading module pylab (matplotlib)...  done!
Loading module numpy...  done!

## Pyevolve v.0.5 - Interactive Mode ##
Press CTRL-D to quit interactive mode.
>>>

As you can see, when you press the ESC Key, a python prompt will be show and the evolution will be paused.

Now, what you can do with this prompt !?

• See all the current population individuals
• Change the individuals
• Plot graphics of the current population
• Data analysis, etc... python is your limit.

Inspecting the population

This is a session example:

## Pyevolve v.0.5 - Interactive Mode ##
Press CTRL-Z to quit interactive mode.
>>> dir()
['__builtins__', 'ga_engine', 'it', 'population', 'pyevolve']
>>>
>>> population
- GPopulation
        Population Size:         80
        Sort Type:               Scaled
        Minimax Type:            Maximize
        Slot [Scale Method] (Count: 1)
                Name: LinearScaling
                Doc:  Linear Scaling scheme

   .. warning :: Linear Scaling is only for positive raw scores



- Statistics
        Minimum raw score                             = 10.00
        Minimum fitness                               = 13.18
        Standard deviation of raw scores              = 2.71
        Maximum fitness                               = 19.92
        Maximum raw score                             = 23.00
        Fitness average                               = 16.60
        Raw scores variance                           = 7.36
        Average of raw scores                         = 16.60

>>> len(population)
80
>>> individual = population[0]
>>> individual
- GenomeBase
        Score:                   23.000000
        Fitness:                 19.920000

        Slot [Evaluator] (Count: 1)
                Name: eval_func
        Slot [Initializator] (Count: 1)
                Name: G1DListInitializatorInteger
                Doc:  Integer initialization function of G1DList

   This initializator accepts the *rangemin* and *rangemax* genome parameters.


        Slot [Mutator] (Count: 1)
                Name: G1DListMutatorSwap
                Doc:  The mutator of G1DList, Swap Mutator
        Slot [Crossover] (Count: 1)
                Name: G1DListCrossoverSinglePoint
                Doc:  The crossover of G1DList, Single Point

   .. warning:: You can't use this crossover method for lists with just one element.



- G1DList
        List size:       50
        List:            [0, 5, 6, 7, 2, 0, 8, 6, 0, 0, 8, 7, 5, 6, 6, 0, 0, 3, 0, 4, 0, 0, 9, 0, 9, 2, 0, 0, 4, 2
, 5, 0, 0, 2, 0, 0, 0, 1, 8, 7, 0, 8, 9, 0, 8, 0, 0, 0, 9, 0]

The exposed modules and objects

The Interaction module is imported with the name “it”, you can see calling the python native dir():

>>> dir()
['__builtins__', 'ga_engine', 'it', 'population', 'pyevolve']

The namespace have the the following modules:

ga_engine

The GSimpleGA.GSimpleGA instance, the GA Engine.

it

The Interaction module, with the utilities and graph plotting functions.

population

The current population.

pyevolve

The main namespace, the pyevolve module.

Using the “it” module

Plotting the current population raw scores histogram

>>> it.plotHistPopScore(population)

Plotting the current population raw scores distribution

>>> it.plotPopScore(population)

Get all the population raw scores

>>> popScores = it.getPopScores(population)
>>> popScores
[17.0, 17.0, 16.0, 15.0, 13.0, 11.0, 11.0, 11.0, 11.0, 11.0, 11.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 9.0,
9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 9.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0, 8.0,
 8.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 7.0, 6.0, 6.0, 6.0, 6.0, 6.0, 5.0, 5.0
, 5.0, 5.0, 5.0, 5.0, 5.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0]

Extending Pyevolve

Creating the representation

The first thing you must do is to see the source code of the GenomeBase.GenomeBase class or the source of the G1DBinaryString.G1DBinaryString class, they are very simple to understand.

Those are the steps to extend Pyevolve with a new representation:

1. Create the chromosome representation class
2. Create the initializator for your chromosome
3. Create the genetic operators
   4. Create the Mutator
   5. Create the Crossover

Well, let’s begin with the creation of the elegant 1D Binary String chromosome, this chromosome is nothing more than a simple array of ‘0’s or ‘1’s, like that: “001001000”.

Note

This 1D Binary String chromosome is an Pyevolve existing chromosome, of course, in the Pyevolve implementation we have more features that we will implement here in this simple example.

All of our new chromosomes must extend the base class called GenomeBase.GenomeBase, this class contains the basic slots for the genetic operators and all the internal stuff that you don’t need to care.

Let’s code the initial draft of our chromosome class:

from GenomeBase import GenomeBase

class G1DBinaryString(GenomeBase):
   pass

As you see, we have imported the GenomeBase.GenomeBase class from the GenomeBase module and we have created the G1DBinaryString class extending the base class.

The next step is to create our constructor method for our class, I’ll show it before and explain later:

def __init__(self, length=10):
   GenomeBase.__init__(self)
   self.genomeString = []
   self.stringLength = length
   self.initializator.set(Consts.CDefG1DBinaryStringInit)
   self.mutator.set(Consts.CDefG1DBinaryStringMutator)
   self.crossover.set(Consts.CDefG1DBinaryStringCrossover)

Well, we start by calling the base class constructor and then creating an internal list to hold our ‘0’s and ‘1’s. It is important to note that we don’t initialize the list, this will be done by our initializator function, and it is because of this that we must keep as an internal attribute the length of your 1D Binary String.

Next, we set our initializator, mutator and crossover to constants, this constants have just the functions of our genetic operators, but if you want, you can set they later, in this example, we will use the defaults for the G1D Binary String.

Note

The attributes self.initializator, self.mutator and self.crossover are all inherited from the GenomeBase class. They are all function slots (FunctionSlot.FunctionSlot).

Now, you must provide the copy() and clone() methods for your chromosome, because they are used to replicate the chromosome over the population or when needed by some genetic operators like reproduction.

The copy() method is very simple, what you need to do is to create a method that copy the contents of your chromosome to another chromosome of the G1DBinaryString class.

Here is our copy() method:

def copy(self, g):
   """ Copy genome to 'g' """
   GenomeBase.copy(self, g)
   g.stringLength = self.stringLength
   g.genomeString = self.genomeString[:]

As you can see, we first call the base class copy() method and later we copy our string length attribute and our internal genomeString, which is our list of ‘0’s and ‘1’s.

Warning

It is very important to note that you must COPY and not just create a reference to the object. On the line that we have the self.genomeString[:], if you use just self.genomeString, you will create a REFERENCE to this object and not a copy. This a simple warning, but can avoid many headaches.

The next step is to create our clone() method, the clone method, as the name says, is a method which return another instance of the current chromosome with the same contents.

Let’s code it:

def clone(self):
   """ Return a new instace copy of the genome """
   newcopy = G1DBinaryString(self.stringLength)
   self.copy(newcopy)
   return newcopy

We simple create a new instance and use the copy() method that we have created to copy the instance contents.

Ready ! We have our first representation chromosome. You can add many more features by implementing python operators like __getitem__, __setitem__.

Creating the initializator

Sorry, not written yet.

Creating the mutator

Sorry, not written yet.

Creating the crossover

Sorry, not written yet.

Snippets

Here are some snippets to help you.

Using two mutators at same time

To use two mutators at same time, you simple add one more to the mutator function slot, like this:

>>> genome.mutator.set(Mutators.G1DListMutatorRealGaussian)
>>> genome.mutator.set(Mutators.G1DListMutatorSwap)

The first line will set the Mutators.G1DListMutatorRealGaussian(), and the second line add one more mutator, the Mutators.G1DListMutatorSwap().

As you can see, it’s very simple and easy, and you will have two mutation operators at same time.

If you want, that just one of this mutators (random picked) be executed at the mutation process, set the random apply parameter of the FunctionSlot.FunctionSlot class to True

>>> genome.mutator.setRandomApply(true)

Using one allele to all list (chromosome) elements (genes)

Sometimes you want to use just one allele type to all genes on the 1D List or other chromosomes, you simple add one allele type and enable the homogeneous flag to True:

>>> setOfAlleles = GAllele.GAlleles(homogeneous=True)
>>> lst = [ "1", "two", 0, 777 ]
>>> a = GAllele.GAlleleList(lst)
>>> setOfAlleles.add(a)

Ready, your setOfAlleles is the GAllele.GAlleles class instance with the lst ([ “1”, “two”, 0, 777 ]) as alleles in all genes.

Changing the selection method

To change the default selection method, you must do this:

>>> ga = GSimpleGA.GSimpleGA(genome)
>>> ga.selector.set(Selectors.GTournamentSelector)

In this example, we are changing the selection method to the Selectors.GTournamentSelector(), the Tournament Selector.

Doing the same evolution on with random seed

Using a random seed, you can guarantee that the evolution will be always the same, no matter the number of executions you make. To initialize the GA Engine with the random seed, use the seed parameter when instantiating the GSimpleGA.GSimpleGA class:

ga_engine = GSimpleGA(genome, 123)

The value 123 will be passed as the random seed of the GA Engine.

Writing the evolution statistics to a CSV File

You can write all the statistics of an evolution to a CSV (Comma Separated Values) fil using the DB Adapter called DBAdapters.DBFileCSV, just create an instance of the adapter and attach it to the GA Engine:

csv_adapter = DBFileCSV(identify="run1", filename="stats.csv")
ga_engine.setDBAdapter(csv_adapter)

Ready ! Now, when you run your GA, all the stats will be dumped to the CSV file. You can set the frequency in which the stats will be dumped, just use the parameter frequency of the DBFileCSV.

Use the HTTP Post to dump GA statistics

With the DBAdapters.DBURLPost, you can call an URL with the population statistics in every generation or at specific generation of the evolution:

urlpost_adapter = DBURLPost("http://localhost/post.py", identify="run1", frequency=100)
ga_engine.setDBAdapter(urlpost_adapter)

Now, the URL “http://localhost/post.py” will be called with the statistics params in every 100 generations. By default, the adapter will use the HTTP POST method to send the parameters, but you can use GET method setting the post paramter to False.

See the mod:Statistics and DBAdapters.DBURLPost documentation.

Using two or more evaluation function

To use two or more evaluation function, you can just add all the evaluators to the slot:

genome.evaluator.set(eval_func1)
genome.evaluator.add(eval_func2)

The result raw score of the genome, when evaluated using more then on evaluation function, will be the sum of all returned scores.

Note

the method set of the function slot remove all previous functions added to the slot.