Genetic Programming - DEAP 1.3.3 documentation (2023)

To see a given function, you can simply print out each candidate solution to see the sequence diagram. However, a character can be generated.

Genetic programming is a specialty of evolutionary computing whose goal is to automatically create programs to solve problems in any domain. Although program development uses different views, the syntax tree is the most common.

Genetic Programming - DEAP 1.3.3 documentation (1)

For example, the above figure represents the program\(\max(x + 3 * y, x +x)\).For this tree and other examples, the leaves of the tree are represented in green as terminals, while internal nodes are represented in red as primitives. Terminals are divided into two subtypes: constants and parameters. Constants remain constant throughout evolution, while parameters are program inputs. For the last tree shown, the arguments are variables\(X\)I\(y\), the constant is a number\(3\).

In DEAP, user-defined primitives and terminals are contained in a primitive pool. There are currently two types of primitive sets: loosely typed and strongly typed.

lost getippter gp

A loosely typed GP does not enforce a specific type between nodes. In particular, a primitive parameter can be any primitive or terminal present in the primitive set.

The following code defines loose typingoriginal setfor the previous tree

pset = original set("main", 2)pset.add primitive(maximum, 2)pset.add primitive(operator.Add to, 2)pset.add primitive(operator.multiple, 2)pset.add terminal(3)

The first line creates a primitive set. Its argument is the name of the procedure that will generate it ("main") and its input quantity 2. The next three lines add the function as a primitive. The first parameter is the add function, the second parameter is the functionquantity.The last line adds a terminal constant. There are currently standard names for parameters"ARG0"I“ARG1”.change it“X”I"yes", just call

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pset.rename parameter(parameter 0=“X”)pset.rename parameter(ARG1="yes")

In this case, all functions accept two parameters. For example, you can have a negate function that takes one argument

pset.add primitive(operator.denial, 1)

Our original set is now ready to generate some trees. thisgeneral practitionerThis module contains three functions for generating prefix expressionsgenFull(),genGrow(), IgenPolaPola().Your first argument is a primitive set. They return valid prefix expressions as a list of primitives. The contents of this list can be read byoriginal treeClass for creating prefix trees.

Express = genFull(pset, at the lowest limit_=1, Maximum_=3)Holtz = original tree(Express)

The last piece of code creates a valid full tree with a height chosen randomly between 1 and 3.

Strogo enters GP

In a strongly typed GP, each primitive and terminal is assigned a specific type. The output type of a primitive must match the input type of another primitive being chained. For example, if a primitive returns a boolean value, the value is guaranteed not to be multiplied by a float if the multiplication operator only works on floats.

definition if_then_else(Entrance, exit 1, Izraz 2): return exit 1 I Entrance others Izraz 2pset = primitive set typing("main", [Boolean value, to wander], to wander)pset.add primitive(operator.XOR, [Boolean value, Boolean value], Boolean value)pset.add primitive(operator.multiple, [to wander, to wander], to wander)pset.add primitive(if_then_else, [Boolean value, to wander, to wander], to wander)pset.add terminal(3,0, to wander)pset.add terminal(1, Boolean value)pset.rename parameter(parameter 0=“X”)pset.rename parameter(ARG1="yes")

In the previous code example, we defined the firstif then othersReturns the function of the second argument if the first argument is true, otherwise returns the third argument. Then we define ourprimitive set typing.The process is also named here"main".The second parameter defines the input type of the program. here,“X”yesBoolean valueI"yes"yesto wander.The third parameter defines the output type of the program asto wander.Adding a primitive to this primitive now requires setting the primitive's input and output types and the terminal. For example, we define our„if_then_else“The first argument acts as a boolean, the second and third arguments must be floating point numbers. The function is defined to return a floating point number. We now understand that the multiplication primitive can only have one terminal3,0, dieif_then_elsefunction or"yes"As input, this is the only floating point number defined.

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Due to type restrictions, the preceding code can create the tree on the left, but not the tree on the right.

Genetic Programming - DEAP 1.3.3 documentation (2)


Tree generation is random, obeying type constraints. If a primitive has an input type that other primitives and terminals cannot provide, it is possible for that primitive to be selected and placed in the tree, causing the tree to fail to complete within the constraints set by the builder. For example, suppose a full tree of height 2 is generated"operate"Take a boolean and float,"I"takes 2 booleans i"negative"Takes a float, terminal is undefined, and the argument is a boolean. The following happens when the terminal cannot be set up to complete the tree.

Genetic Programming - DEAP 1.3.3 documentation (3)

In this case, DEAP triggers aindex errormessage"thisgp. generate Function tried again Add to A terminal outside of top wandering, Ali There yesNo Accessibility. "

permanent ephemeral

A temporary constant is a terminal that encapsulates a value generated by a particular function at runtime. Temporary constants allow you to use terminals that don't all have the same value. For example, create a temporary constant that takes the value of\([-1, 1)\)we use

pset.add temporary constant(lambda: coincidentally.uniform(-1, 1))

The value of a temporary constant is determined when it is inserted into the tree, and never changes unless it is replaced by another temporary constant. Since it's a terminal, it can also be entered as a temporary constant.

pset.add temporary constant(lambda: coincidentally.Landing(-10, 10), integer)
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Creation of Individual Trees

The code presented in the last two sections produces efficient trees. However, ifOperators and AlgorithmsGuidance, these trees are not yet valid individuals for evolution. You must combine creators and toolboxes to produce valid entities. we have to workhealthyIIndividuallyclass. We add a reference to the base setIndividuallyFitness supplements. This is what some GPs use for substitutions.

creator.create(„Fitness minutes“, base.healthy, Weights=(-1,0,))creator.create("individual", general practitioner.original tree, minutes, pset=pset)

Then we register the generator function to atoolbox.

toolbox = base.toolbox()toolbox.register("Express", general practitioner.genFull, pset=pset, at the lowest limit_=1, Maximum_=3)toolbox.register("individual", tool.initialization iteration, creator.Individually, toolbox.Express)

I calltoolbox.individual()Easily return a single typeoriginal tree.

tree classification

In DEAP, trees can be translated into human-readable Python code and Python code objects, usinggeneral practitionermodule. first functionstrait()Takes an expression or a PrimitiveTree and converts it to human-readable Python code. For example, the following lines of code generate a tree and print the first example of a raw sentence.

>>>Express = genFull(pset, at the lowest limit_=1, Maximum_=3)>>>Holtz = original tree(Express)>>>the strait(Holtz)'multiply(add(x,x), max(y,x))'

This string represents the program we just generated but cannot run yet. To make it executable, we need to convert the expression into a Python code object. Because this function has two inputs, we want to turn the code into a callable. this is possiblewrite()The function takes two arguments: the expression to translate and the associated original set. The following example compiles the preceding tree and evaluates the resulting function\(x=1\)I\(y=2\).

>>>Function = write(Holtz, pset)>>>Function(1, 2)4
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If the generated program has no input parameters, the expression can be specified with awrite()Function. An example of such a problem isThe problem with artificial ants.

Limit tree size and control bloat

Because DEAP uses the Python parser to compile the code for the tree representation, it inherits its limitations. The most common limitation is the parsing heap limit. The parser stack limit of the Python interpreter is usually between 92 and 99. This means that an expression can contain up to 91 consecutive primitives. In other words, the maximum depth a tree can be is 91. If the limit is exceeded, Python will report the following error

s_to recommended: parser staple food overflowtrace back (most the youngest financial support Last):[...]memory error

Since this limit is hardcoded in the interpreter, there is no easy way to increase it. Also, this error is often due to a phenomenon known as GPas inflation. That is, the resulting individual has reached the point where it contains too many primitives to effectively solve the problem. This problem leads to evolutionary stagnation. To address this, DEAP provides several functions that effectively constrain the size and height of trees below acceptable limits. These operators are listed in the GP areaoperator.

draw tree the elements needed to draw a dendrogramNetworkXorgraphic visualization.graph function validoriginal treeobject and returns a list of nodes, a list of edges, and a dictionary associating a label with each node. It can be used with pygraphviz as follows.

outside of deep import base, creator, general practitionerpset = general practitioner.original set("main", 1)pset.add primitive(operator.Add to, 2)pset.add primitive(operator.pod, 2)pset.add primitive(operator.multiple, 2)pset.rename parameter(parameter 0='X')creator.create("individual", general practitioner.original tree)toolbox = base.toolbox()toolbox.register("Express", general practitioner.genPola, pset=pset, at the lowest limit_=1, Maximum_=2)toolbox.register("individual", tool.initialization iteration, creator.Individually, toolbox.Express)Express = toolbox.Individually()node, both sides, Label = general practitioner.Graff(Express)### Graphviz section ###import graphic visualization if pgvG = sides)G.schedule(avant-garde="View")for and you node: no = G.get node(and) no.Attributes["mark"] = Label[and]G.pull("Tree.pdf")

When using NetworkX, the last part becomes:

import matplotlib.pyplot if pltimport NetworkX if nxG = sides)Location = nx.graphviz_layout(G, avant-garde="View")nx.draw network nodes(G, Location)nx.draw mesh edges(G, Location)nx.draw grid label(G, Location, Label)plt.exhibit()
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Depending on the version of graphviz, nodes may appear in an unpredictable order. In case of two graphs of the same tree, the associated nodes can be swapped. This does not affect the display or numerical results of the underlying tree.

How to develop a program

Various possibilities for program tree development are proposedGenetic Programming (GP)example.


What is strongly typed GP in DEAP? ›

Strongly Typed GP

The output type of a primitive must match the input type of another one for them to be connected. For example, if a primitive returns a boolean, it is guaranteed that this value will not be multiplied with a float if the multiplication operator operates only on floats.

What is an ephemeral constant in DEAP? ›

An ephemeral constant is a no argument function that returns a random value. The value of the constant is constant for a Tree, but may differ from one Tree to another.

What is the mutation function in DEAP? ›

Mutation. This function applies a gaussian mutation of mean mu and standard deviation sigma on the input individual. This mutation expects a sequence individual composed of real valued attributes. The indpb argument is the probability of each attribute to be mutated.

Which programming language is best for genetic algorithms? ›

C++: C++ is one of the best choices for genetic programming as they are highly computationally intensive. It provides a high-level of software environment to do complicated work in genetic programmings such as tree-based GP, integer-valued vector, and real-valued vector genetic algorithms, evolution strategy and more.

What are four techniques used in genetic algorithms? ›

(GA)s are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover (also called recombination).

What is the difference between strongly typed and dynamically typed? ›

Strong typing means that variables do have a type and that the type matters when performing operations on a variable. Dynamic typing means that the type of the variable is determined only during run time.

What is strongly typed vs loosely typed? ›

In computer programming, a programming language is strongly typed if it demands the specification of data types. A programming language is loosely typed, or weakly typed, when it does not require the explicit specification of different types of objects and variables.

What is the difference between genetic algorithm and genetic programming? ›

The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic programming creates computer programs in the lisp or scheme computer languages as the solution. Genetic algorithms create a string of numbers that represent the solution.

What are the 4 types of mutation? ›

Mutations can be of many types, such as substitution, deletion, insertion, and translocation.

What are the three types of mutations? ›

Types of Mutations

There are three types of DNA Mutations: base substitutions, deletions and insertions.

What disease is caused by deletion mutation? ›

Deletion. Deletion mutations are actually the cause for a large number of genetic diseases, such as two-thirds of cystic fibrosis cases and the cat cry syndrome, which is so-called because children with this syndrome often have a cry that sounds similar to a cat meowing.

How hard is genetic algorithm? ›

Genetic algorithms are simple to implement, but their behavior is difficult to understand. In particular, it is difficult to understand why these algorithms frequently succeed at generating solutions of high fitness when applied to practical problems.

What is better than genetic algorithm? ›

Genetic algorithms usually perform well on discrete data, whereas neural networks usually perform efficiently on continuous data. Genetic algorithms can fetch new patterns, while neural networks use training data to classify a network.

Are genetic algorithms still useful? ›

Genetic algorithms are one of the most fundamental algorithms in computer science. Consequently, they have found many applications in the real world in different industries and for different tasks.

Who was the father of genetic algorithms? ›

The genetic algorithm (GA), developed by John Holland and his collaborators in the 1960s and 1970s [11,4], is a model or abstraction of biological evolution based on Charles Darwin's theory of natural selection.

What are the limitations of genetic algorithm? ›

What Are The Disadvantages Of Genetic Algorithm
  • Genetic algorithms are often criticized for being too slow. There are several disadvantages of using genetic algorithms. ...
  • They can be expensive to implement. ...
  • They can be difficult to understand. ...
  • They can be difficult to debug. ...
  • They can be difficult to optimize.

Is Python strongly or weakly typed? ›

Python is both a strongly typed and a dynamically typed language. Strong typing means that variables do have a type and that the type matters when performing operations on a variable. Dynamic typing means that the type of the variable is determined only during runtime.

Why is it called duck typing? ›

This term comes from the saying “If it walks like a duck, and it quacks like a duck, then it must be a duck.” (There are other variations). Duck typing is a concept related to dynamic typing, where the type or the class of an object is less important than the methods it defines.

Why is C not strongly typed? ›

C++ has always struck me as one of the most highly typed languages available. So I was surprised to see that C++ is weakly typed. Apparently, Because type-casting allows one to interpret an integer field of a structure as a pointer, C and C++ are considered loosely typed.

What is an example of a strongly typed programming language? ›

Examples of strongly typed languages in existence include Java, Ruby, Smalltalk and Python. In the case of Java, typing errors are detected during compilation Other programming languages, like Ruby, detect typing errors during the runtime.

Why are strongly typed languages better? ›

The advantage of strongly typed languages is that the compiler can detect when an object is being sent a message to which it does not respond. This can prevent run-time errors. The other advantages of strong typing are: earlier detection of errors speeds development.

Is C++ a strongly typed programming language? ›

C++ is more strongly typed than C because it has parametric polymorphism (through templates), letting you create generic data types that are still accurately typed. Python is not as strongly typed as C++ because it can't accurately represent such types. C++ may have loopholes, but Python's type system is still weaker.

What is genetic programming with example? ›

Genetic Programming is a new method to generate computer programs. It was derived from the model of biological evolution. Programs are 'bred' through continuous improvement of an initially random population of programs.

What are the two main features of genetic algorithm? ›

What are the two main features of Genetic Algorithm? Explanation: Fitness function helps choosing individuals from the population and Crossover techniques defines the offspring generated.

What does C mean in genetics? ›

​Genetic Code

Each gene's code uses the four nucleotide bases of DNA: adenine (A), cytosine (C), guanine (G) and thymine (T) — in various ways to spell out three-letter “codons” that specify which amino acid is needed at each position within a protein.

What is the most serious type of mutation? ›

Frameshift mutations are generally much more serious and often more deadly than point mutations. Even though only a single nitrogen base is affected, as with point mutations, in this instance, the single base is either completely deleted or an extra one is inserted into the middle of the DNA sequence.

What are 3 factors that can cause DNA mutations? ›

Mutations can result from errors in DNA replication during cell division, exposure to mutagens or a viral infection. Germline mutations (that occur in eggs and sperm) can be passed on to offspring, while somatic mutations (that occur in body cells) are not passed on.

What three genetic disorders are caused by mutations? ›

Neurofibromatosis type 1 (NF1). Sickle cell disease. Tay-Sachs disease.

What are the 3 single gene mutations? ›

Some of the more common single-gene disorders include cystic fibrosis, hemochromatosis, Tay-Sachs, and sickle cell anemia. Even though these diseases are primarily caused by a single gene, several different mutations can result in the same disease but with varying degrees of severity and phenotype.

What are 7 genetic disorders? ›

Genetic Disorders
  • Genetic Disorders. Sickle Cell Disease.
  • Cystic fibrosis. Cystic Fibrosis Liver Disease.
  • Brain, Nerves and Spine. Huntington's Disease.
  • Cleft lip and palate. Cleft Lip and Palate.

What is the most common deletion mutation? ›

Deletions of one or more exons are the most common type of mutation. Since there are a total of 79 exons in the dystrophin gene, there are many different deletions that can occur. However, there are certain areas of the gene that are more likely to have a deletion, and these areas are called “hot spots”.

What is Angel Man's disease? ›

Angelman syndrome is a complex genetic disorder that primarily affects the nervous system. Characteristic features of this condition include delayed development, intellectual disability, severe speech impairment, and problems with movement and balance (ataxia).

What is the best Python library for genetic algorithm? ›

PyGAD is an open-source Python library for building the genetic algorithm and optimizing machine learning algorithms. It works with Keras and PyTorch. PyGAD supports different types of crossover, mutation, and parent selection operators.

How to implement genetic algorithm in Python? ›

Implementation of Genetic Algorithm in Python
  1. #initialize population. import random. best=-100000. ...
  2. #fitness score calculation ............ def fitness_score() : global populations,best. ...
  3. def selectparent(): global parents. ...
  4. def crossover() : global parents. ...
  5. def mutation() : global populations, parents.
Apr 19, 2020

Which score is used to find the best solution in genetic algorithm? ›

Fitness Function (also known as the Evaluation Function) evaluates how close a given solution is to the optimum solution of the desired problem. It determines how fit a solution is.

What are the steps of genetic algorithm? ›

Five phases are considered in a genetic algorithm:
  • Initial population.
  • Fitness function.
  • Selection.
  • Crossover.
  • Mutation.

Which Python version is best for AI? ›

Now that you know why Python is one of the top programming languages, here are the 10 best python libraries for machine learning and AI:
  • NumPy. NumPy is widely regarded as the best Python library for machine learning and AI. ...
  • SciPy. ...
  • Theano. ...
  • Pandas. ...
  • TensorFlow. ...
  • Keras. ...
  • PyTorch. ...
  • Scikit-Learn.
Jun 25, 2022

What is the best source to learn data structures and algorithms in Python? ›

Python Data Structures [Coursera Best Course]

This course is also part of Coursera's most popular Python for Everybody Specialization and its offered by the University of Michigan. This course is delivered by Charles Russell Severance and it has on average a 4.9 rating from more than 50K reviews which is phenomenal.

Which Python version is best for artificial intelligence? ›

Python Version: You can use Python 3.6 or higher but Python 3.9 is recommended.


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