NSGA-II | 多目标优化遗传算法，python实现 & 动态展示过程
kandi X-RAY | NSGA-II Summary
kandi X-RAY | NSGA-II Summary
多目标优化遗传算法，python实现&动态展示过程
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of NSGA-II
NSGA-II Key Features
NSGA-II Examples and Code Snippets
Community Discussions
Trending Discussions on NSGA-II
QUESTION
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 15:40The 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.
QUESTION
I am currently running some algorithms to solve a multi-objective linear mathematical model (Operation Research). I've used three algorithms: Constraint Method (C-M), Non-Sorting Genetic Algorithm II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). I've set a performance indicator as to the number of the solution in the Pareto Border (No_solutions) and I gathered data from six instances (I1,..,I6).
My data is the following table:
...ANSWER
Answered 2019-Jul-03 at 05:23Take a closer look at the documentation for the aov function. It states that F and p-values are only shown if you have non-zero residual degrees of freedom in your model
In your case you have 18 data points, your predictors are using up 17 (7+5+5) degrees of freedom, and 1 is required for the model itself, which brings you to 18. This leaves you with 0 residual degrees of freedom and therefore the F-value is not displayed. The solution is to collect more data or simplify your model
QUESTION
Are there any multiobjective sorting algorithms built into Pandas?
I have found this which is an NSGA-II algorithm (which is what I want), but it requires passing the objective functions in as separate files. In an ideal world, I would use a DataFrame for all of the data, call a method like multi_of_sort on it while specifying the objective function columns (and other required parameters), and it would return another DataFrame with the Pareto optimum values.
This seems like it should be trivial with Pandas, but I could be wrong.
...ANSWER
Answered 2019-Mar-29 at 19:14As it turns out... the pareto package referenced above does handle DataFrame inputs.
QUESTION
I am using evolutionary algorithms e.g. the NSGA-II algorithm to solve unconstrained optimization problems with multiple objectives.
As my fitness functions sometimes have very different domains (e.g. f1(x) generates fitness values within [0..1] and f2(x) within [10000..10000000]) I am wondering if this has an effect on the search behaviour of the selected algorithm.
Does the selection of the fitness function domain (e.g. scaling all domains to a common domain from [lb..ub]) impact the solution quality and the speed of finding good solutions? Or is there no general answer to this question?
Unfortunately, I could not find anything on this topic. Any hints are welcome!
...ANSWER
Answered 2018-Jul-15 at 11:22Your question is related to the selection strategy implemented in the algorithm. In the case of the original NSGA II, selection is made using a mixture of pareto rank and crowding distance. While the pareto rank (i.e. the non dominated front id of a point) is not changing scaling the numerical values by some constant, the crowding distance does.
So the answer is yes, if your second objective is in [10000 .. 10000000] its contribution to the crowding distance might be eating up the one of the other objective.
In algorithms such as NSGA II units count!
QUESTION
I am working on optimization using evolutionary algorithm, specifically SPEA-II. I was curious if there is any algorithm named SPEA-III. I tried googling but I could only find a proposed modification of the SPEA-II using reference search direction. Is it same as SPEA-III, in case of NSGA-III, this is the probably only difference from NSGA-II.
...ANSWER
Answered 2017-Jul-05 at 06:24I was able find a reference paper on SPEA3, the authors have proposed what they called a generalization of SPEA2 algorithm and name it as SPEA3.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install NSGA-II
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
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
