Hi Bondhugon! these are the work of my college Sixth Semester lab (Soft Computing, Computer Networks, and Weka). You can check the codes here.

A collection of Benchmark functions for numerical optimization problems

Type 1 and Interval Type 2 Fuzzy Logic Systems in Python

fuzzylite: a fuzzy logic control library in C++

pyfuzzylite: a fuzzy logic control library in Python

MATLAB code for Teaching Learning Based Optimization Algorithm (minimization and maximization)

Contains all the research papers Harshita Gupta has been a part of in her Undergraduate Degree of Bachelor of Technology in Information Technology

An Improved Support Vector Machine for medical diagnosis using Whale Optimization Algorithm ( Bio-Inspired, Meta-Heuristic ) for Hyperparameter Optimization ( C, Gamma).

The International Journal of Soft Computing and Engineering (IJSCE) has ISSN: 2231-2307 (online), which is an online, open access, peer reviewed, periodical bi-monthly international journal.

CSC703: Artificial Intelligence and Soft Computing [AISC] & CSL703: Artificial Intelligence and Soft Computing Lab [AISC Lab] <Semester VII>

rsfuzzylite: a fuzzy logic control library in Rust

Windows Form, Soft Computing - Evolutionary algorithm solving a knapsack problem

My BE CSE Chitkara University Lab Practical Codes and Scripts

Visual Question Answering with added novel Semantic Analysis approach. Inspired in part by Armored Core 6: Fires of Rubicon.

Implementation of Learning Vector Quantization (LVQ) and Extreme Learning Machine (ELM) with Iris Dataset

Binary classification of datapoints using Single Layer Perceptron (SLP) algorithm

It's a Java-based application that provides functions and tools for designing and simulating fuzzy logic systems. It offers a user-friendly interface for creating and testing fuzzy logic systems by allowing users to define and configure input variables, output variables, membership functions, rules, and defuzzification methods.

This repository contains a C++ program that solves the Knapsack Problem using a Genetic Algorithm. The Knapsack Problem is a classic optimization problem where we aim to maximize the total value of items to be packed in a knapsack, given the knapsack's weight capacity and a set of items with their respective weights and values.

This program implements a genetic algorithm for curve fitting using a polynomial equation. The goal is to find the best coefficients for the polynomial equation that minimize the distance between the curve and a given set of data points. The genetic algorithm is used to search for the optimal solution by evolving a population of candidate solutions

Project for my college course Nature Inspired Computing