GKNAP: A Java and C++ package for solving the multidimensional knapsack problem

A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in Chu and Beasley, but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

(Removed windowing) A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in Chu and Beasley, but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

(Fixed C++ code) (Added paper) (Added data.DAT) (Added Java Implementation) A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in Chu and Beasley, but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

(Added paper) (Added data.DAT) (Added Java Implementation) A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in [Chu98], but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

(Added data.DAT) (Added Java Implementation) A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in [Chu98], but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

(Added Java Implementation) A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in [Chu98], but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).

Genetic Algorithm for the 0/1 Multidimensional Knapsack Problem

A genetic algorithm implementation for the multidimensional knapsack problem. The multi-constraint (or multidimensional) knapsack problem is a generalization of the 0/1 knapsack problem. The multi-constraint knapsack problem has m constraints and one objective function to be maximized while all the m constraints are satisfied.

The implementation is similar to the one described in [Chu98], but it's significantly different. It uses Lagrangian multipliers as constraint weights and compared to the paper, it finds close to optimum solutions much faster. (Convergence can be controlled using the parameters).