An evolutionary solver for linear integer programming
This addresses optimization problems in operations research and engineering, but it is incremental as it builds on existing evolutionary and linear programming methods.
The paper tackles the problem of solving linear integer programs by introducing an evolutionary algorithm that separates integer and continuous variables, using a linear program solver for the latter. Results show promising performance, with good feasible solutions and outperforming branch-and-bound in some difficult benchmark tests.
In this paper we introduce an evolutionary algorithm for the solution of linear integer programs. The strategy is based on the separation of the variables into the integer subset and the continuous subset; the integer variables are fixed by the evolutionary system, and the continuous ones are determined in function of them, by a linear program solver. We report results obtained for some standard benchmark problems, and compare them with those obtained by branch-and-bound. The performance of the evolutionary algorithm is promising. Good feasible solutions were generally obtained, and in some of the difficult benchmark tests it outperformed branch-and-bound.