MathGO

Integer Linear Programming Mathematical Genetic Optimizer can be used to solve problems with up to 12 variables and up to 11 constraints. The solver is based on evolutionary Genetic Algorithm and heuristic neighbour search that work in collaboration in order to find maximum or minimal values to an objective function subject to a set of constraints. Typical problems can be found in most of the Mathematics with Applications at University level books, linear programming chapter. Where usually they solve problems applying the graphical or Simplex method. The areas where this kind of problems range from social science, business, natural science, finance, manufacturing, and transportation, among others. The app has been conceived to solve Integer Linear Programming problems but with a slight modification to the mathematical model it can be used to solve Linear Programming problems with a reasonable number of decimal places. The out put is a list of solutions with best solution at the top of the list. The app runs in a progressive way that means if after an output list has been obtained clicking the Find Solution button again will continue finding for optima using the results from previous run.
The results need to be interpreted accordingly to the problem and the user is the sole responsible of any decision making regarding the output provided by this app.
The genetic algorithm is not the fastest algorithm out there to solve this kind of problems so there will be problems where it requires to run multiple times requiring longer time to make sure that if not optimal a good approximation is obtained.
The app has been tested using problems from Mathematics books at university level and it was able to solve the problems in times ranging from less than a second for 2 and 3 variables and less than 50 seconds in the worst cases of problems with 4 variables. The time to obtain a solution depends highly on the parameter setting and lucky because most of the mechanism are based on random. This means that there could be problems where a solutions is found in few second but some other times could go much higher requiring multiple runs.
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