Bin Packing Problem using Neural Combinatorial Optimization.

Genetic Algorithm for Knapsack Problem

Leverage the power of modern C++ to build robust and scalable applications

Julia and Python recursion algorithm, fractal geometry and dynamic programming applications including Edit Distance, Knapsack (Multiple Choice), Stock Trading, Pythagorean Tree, Koch Snowflake, Jerusalem Cross, Sierpiński Carpet, Hilbert Curve, Pascal Triangle, Prime Factorization, Palindrome, Egg Drop, Coin Change, Hanoi Tower, Cantor Set, Fibo…

Evolutionary Algorithm for the 2D Packing Problem combined with the 0/1 Knapsack Problem (Master Thesis)

Auto Parallelization - runs test files in multiple jobs

Algorithms for solving knapsack problems with Python

Julia package to solve Knapsack problems

Exact and meta-heuristic algorithms for NP problems

An example pipeline that runs parallel tests for a Rails app using Docker and Knapsack.

Optimize multiple knapsack problem using reinforcement learning.

A module for resource allocation solving

A comprehensive collection of my personal coding work — from algorithm implementations to problem-solving practice and competitive programming solutions. This repository serves as a digital archive of my learning journey in Computer Science and Engineering.

Evolutionary Multitasking in Permutation-Based Combinatorial Optimization Problems: Realization with TSP and Knapsack

All DSA-2 topics are covered in the UIU DSA-2 course, including both lab and theory courses. Check DSA-1 Topics: https://github.com/TashinParvez/DSA_1_UIU

This repository was created for the subject of Computer Theory. The propose of this subject is to improve your skills to solve the 0-1 knapsack problem of different ways. The techniques used were Dynamic Programing and two metaheuristics (which are GRASP and TABU search).

Knapsack algorithms

Classical algorithm implementation.

Heuristic solution for Knapsack Container Loading Problem (KCLP) implemented in C++ with visualization in python.

Fixed linear ramp schedules in QAOA constitute a universal set parameters, i.e., a set of γ and β parameters that rapidly approximate the optimal solution, x∗, independently of the COP selected, and that the success probability of finding it, probability(x∗), increases with the number of QAOA layers p.