How Evolver Works

Evolver uses OptQuest and Genetic Algorithms to search for optimum solutions to a problem. In addition, if it determines that a problem is linear, it uses linear programming algorithms.

OptQuest uses metaheuristic, mathematical optimization, and neural network components to guide the search for optimal solutions to decision and planning problems of all types. OptQuest’s methods integrate state-of-the-art metaheuristic procedures, including Tabu Search, Neural Networks and Scatter Search into a single composite method.

Genetic Algorithms (GAs) provide an alternative solution method. GAs mimic Darwinian principles of natural selection by creating an environment where hundreds of possible solutions to a problem can compete with one another, and only the “fittest” survive. Just as in biological evolution, each solution can pass along its good “genes” through “offspring” solutions so that the entire population of solutions will continue to evolve better solutions.

Lastly, Evolver uses a special-purpose algorithm to solve linear models much more quickly than if they were solved with general-purpose algorithms. (This requires that Optimization Mode be left as Automatic in the Engine tab of the Optimization Settings dialog).