/Feature-Optimization-Using-Genetic-Algorithm-

Implemented and optimized the solution of knapsack problem using genetic algorithm

Primary LanguagePython

Feature-Optimization-Using-Genetic-Algorithm-

KNAPSACK PROBLEM

The Knapsack problem helps in achieving Local optimized problem, but Genetic Algorithm helps in achieving Global optimized problem. The main motive behind implementing this project is to optimize the feature selection.

Apply the Genetic Algorithm for optimization on a dataset obtained from UCI ML repository

PROCESSES:

Mutation:

Mutation is the part of the GA which is related to the “exploration” of the search space. It has been observed that mutation is essential to the convergence of the GA while crossover is not.

Crossover:

The crossover operator is analogous to reproduction and biological crossover. In this more than one parent is selected and one or more off-springs are produced using the genetic material of the parents. Crossover is usually applied in a GA with a high probability