This is an implementation of Fuzzy Rough Dependency Degree (FRDD) to calculate the importance of selected feautres

Code for optimization of GA for feature selection

Suppose, you are the owner of a bank that operates in a strange way. Customers can lend money from your bank (just like a normal bank) and they can also deposit money in your bank. A register is maintained to track the daily transactions. However, being the strange owner of a strange bank, you have a fascination with finding out whether a portio…

Fitness Functions oriented for Frontend Development

CS4106 Genetic Algorithm project. Use a genetic algorithm to minimize graph edge crossings.

Simple (archaic) method to transfer images from Processing to Python and get their evaluation or fitness in return

A simple genetic algorithm that solves the one-max problem

Neural Network implementation of Flappy Dot

Configurable genetic algorithm for solving triangle classification problem

Fitness Exercises App using two separate APIs! With the functionality to choose exercise categories and specific muscle groups, browse more than one thousand exercises with practical examples, pagination, exercise details, pull related videos from YouTube, display similar exercises, and much more.

Genetic Algorithms techniques in solving a searching problem for optimization.

A stock market prediction model using Genetic algorithm (coded in Lisp)

Powershell module for genetic algorithm.

Some part of USPEX source code is published here to help users to interface new codes with USPEX, or debug the previous interfaces. Now, It is also possible to add new fitness functions to USPEX. This will allow users to search for any property that they want using evolutionary algorithm USPEX.

Genetic Algorithms techniques in solving a searching problem for optimization.

Parallelized genetic algorithm implementation using pthreads and java-concurrency.

Implementation and evaluation of the Ant Colony Optimization algorithm on the bin-packing problem

Implementation of a genetic algorithm to solve the Knapsack problem with a capacity C and a given set of N objects. The genetic fitness function sums up the profits of the objects in the Knapsack.

Neuroevolutionary game.

This is a simulation of sunfllowers evolution given certain evniormental conditions, it is simulated via a genetic algorthim using a loss/fitness function, corssing over and other biological mechanisms.