Interactive Genetic Algorithm Knapsack Solver

A web-based educational tool using Python and Dash to visualize how evolutionary computation solves the classic Knapsack optimization problem.

Python
Dash
Data Science & Analytics
Genetic Algorithms (GA)
Heuristics
Plotly
Algorithms

Overview

This project is an interactive web application designed to solve the classic Knapsack Problem using a Genetic Algorithm (GA). Built with Python and the Dash framework, it provides a dynamic and visual way to understand how bio-inspired heuristics can tackle complex optimization challenges.

The Problem

The Knapsack Problem is a famous combinatorial optimization problem: given a set of items, each with a weight and a value, determine which items to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.

How It Works: Genetic Algorithm

The solution implements an evolutionary approach inspired by natural selection:

  1. Initialization: Creates a random population of "genomes" (binary strings representing item selection).
  2. Fitness Evaluation: Calculates the total value of the selected items (penalizing over-capacity solutions).
  3. Selection: Uses tournament selection to pick the best "parents."
  4. Crossover & Mutation: Combines parents to create offspring and introduces random mutations to maintain diversity.

Key Features

  • Interactive Interface: Users can input custom item values, weights, and capacity constraints via a web UI.
  • Dynamic Visualization: Uses Plotly to generate real-time graphs showing how the "fitness" (solution quality) improves over generations.
  • Detailed Analytics: Displays the best solution found, total value, total weight, and specific items selected.

Tech Stack

  • Core Logic: Python (NumPy, Pandas)
  • Web Framework: Dash (by Plotly)
  • Visualization: Plotly
  • Testing: Unittest