Academic Journals Database
Disseminating quality controlled scientific knowledge

A NEW GENETIC ALGORITHM TO SOLVE KNAPSACK PROBLEMS

ADD TO MY LIST
 
Author(s): Derya TURFAN | Cagdas Hakan ALADAG | Ozgur YENIAY

Journal: Journal of Social and Economic Statistics
ISSN 2285-388X

Volume: 1;
Issue: 2;
Start page: 40;
Date: 2012;
VIEW PDF   PDF DOWNLOAD PDF   Download PDF Original page

Keywords: Genetic algorithms | Mean-variance optimization | Portfolio analysis | knapsack problem

ABSTRACT
Knapsack problem is a well-known class of optimization problems, which seeks to maximize the benefit of objects in a knapsack without exceeding its capacity. Various knapsack problems have been tried to be solved by decision makers a wide range of fields, the most important one of these is portfolio optimization. In recent few decades, heuristic methods have been widely used to solve hard optimization problems since they have proved their success in many real life applications. Genetic algorithm is one of these heuristic algorithms which have been successfully employed in a variety of continuous, discrete and combinatorial optimization problems. In this study, a new genetic algorithm is improved to solve a portfolio optimization problem efficiently.
RPA Switzerland

RPA Switzerland

Robotic process automation

    

Tango Jona
Tangokurs Rapperswil-Jona