Hybrid Evolutionary/Classical Solver
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The hybrid Evolutionary Solver included free with the Premium Solver Platform allows you to solve non-smooth optimization problems -- for example using IF, CHOOSE or LOOKUP functions -- that cannot be handled effectively by the standard Excel Solver. It also handles integer variables and the "alldifferent" constraint. But this Solver is much more than a genetic or evolutionary algorithm -- it also uses "classical" optimization methods to solve for constraints and to conduct local searches for improved solutions. The result is breakthrough performance, better than virtually any genetic or evolutionary algorithm alone. |
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To learn more, click on Genetic Algorithms and Evolutionary Algorithms - Introduction.
The Premium Solver Platform's hybrid Evolutionary/Classical Solver finds good solutions to problems involving arbitrary Excel functions, even user-written functions. And where a "classical" nonlinear Solver would find only a locally optimal solution, this hybrid Solver will often find globally optimal -- or near-optimal solution.
The hybrid Evolutionary/Classical Solver uses genetic algorithm methods such as mutation, crossover, selection and constraint repair, but it also uses deterministic, gradient-free direct search methods, classical gradient-based quasi-Newton methods, and even the Simplex method for linear subsets of the constraints. The classical methods sometimes yield rapid local improvement of a trial solution, and they also help to solve for sets of constraints. This enables the hybrid Evolutionary / Classical Solver to handle problems with many constraints, which are typically beyond the capabilities of genetic and evolutionary algorithms alone.
New Filtered Multistart Methods
The Premium Solver Platform includes four different local search methods, ranging from Randomized Local Search and Deterministic Pattern Search to Nonlinear Gradient and Linear Local Gradient Search methods. Version 6.0 can choose the best local search method automatically. What's more, in Version 6.0 the Evolutionary Solver applies a "distance filter" and a "merit filter" to determine whether to spend time conducting a local search when a promising new point is found by the genetic algorithm methods. These filters can greatly improve the Evolutionary Solver's speed on global optimization problems, and the quality of the solutions it obtains.
New Population Report
The Premium Solver Platform also includes a new Population Report for problems solved with the Evolutionary Solver. (Click on the worksheet to see it full size.)
Where the Answer Report gives you detailed information about the single "best solution" returned by the Solver, the Population Report gives you summary information about the entire population of candidate solutions maintained by the Evolutionary Solver at the end of the solution process.
The Population Report can give you insight into the performance of the Evolutionary Solver as well as the characteristics of your model, and help you decide whether additional runs of the Evolutionary Solver are likely to yield even better solutions.