NSGA-II | python implementation of NSGA-II algorithm | Machine Learning library

by haris989 Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | NSGA-II Summary

NSGA-II is a Python library typically used in Artificial Intelligence, Machine Learning, Example Codes applications. NSGA-II has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However NSGA-II build file is not available. You can download it from GitHub.

This is a python implementation of NSGA-II algorithm. NSGA is a popular non-domination based genetic algorithm for multi-objective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGA II was developed, which has a better sorting algorithm , incorporates elitism and no sharing parameter needs to be chosen a priori.

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Support

NSGA-II has a low active ecosystem.

It has 291 star(s) with 144 fork(s). There are 10 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 0 have been closed. There are 5 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of NSGA-II is current.

Quality

NSGA-II has 0 bugs and 0 code smells.

Security

NSGA-II has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

NSGA-II code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

NSGA-II is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

NSGA-II releases are not available. You will need to build from source code and install.

NSGA-II has no build file. You will be need to create the build yourself to build the component from source.

NSGA-II saves you 46 person hours of effort in developing the same functionality from scratch.

It has 123 lines of code, 8 functions and 1 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed NSGA-II and discovered the below as its top functions. This is intended to give you an instant insight into NSGA-II implemented functionality, and help decide if they suit your requirements.

Sorts two values .
Compute the distance between two values .
Sort a list .
Perform crossover .
Returns the index of an element in a list .
Makes mutation .
Compute the value of a function .
2nd function .

Get all kandi verified functions for this library.

NSGA-II Key Features

No Key Features are available at this moment for NSGA-II.

NSGA-II Examples and Code Snippets

No Code Snippets are available at this moment for NSGA-II.

Community Discussions

Trending Discussions on NSGA-II

Pymoo generating candidates with nan parameters

How the number of decision variables have effects on the populations in NSGA-II?

QUESTION

Pymoo generating candidates with nan parameters

Asked 2022-Feb-08 at 21:32

I'm running a multi-objective optimisation with Pymoo (0.5.0) using NSGA-III and within my population of new candidates some of the generated candidates have nan parameters. This results in my evaluate function (which is a call to a neural network) returning nan. The optimisation is running and producing desired results but I'd like to know why some of the candidate parameters are nan. Here is the code for the problem.

Problem setup:

...

ANSWER

Answered 2021-Oct-08 at 10:38

The nan arise because the limits for your parameters 11, 12 and 12 are equal (-1 and -1 in all cases).

If you look at the code for the polynomial mutation (real_pm), you have the following lines:

Source https://stackoverflow.com/questions/69392226

QUESTION

How the number of decision variables have effects on the populations in NSGA-II?

Asked 2021-Apr-14 at 15:40

I just have started reading the NSGA-II code in Matlab recently, and I don't understand what the number of decision variables setting relates in the initialization state in genetic algorithm. Is it related to the test function or used for other intention?

I would appreciate it if you would be so kind to answer.

...

ANSWER

Answered 2021-Apr-14 at 15:40

The number of decision variables is related to the number of genes in the chromosomes of each individual.

Let's say you are trying to optimize a function f(x,y). Then you have two decision variables, and therefore your chromosomes will be R^d where d = 2.

Knowing the number of decision variables is essential to the metaheuristics such as genetic algorithm because much of its operators rely on it, e.g., to perform crossover you need to know the size of the chromosome (size of your representation) so you can iterate and create the offspring, etc.

Source https://stackoverflow.com/questions/67059669

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install NSGA-II

You can download it from GitHub.
You can use NSGA-II like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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