xlOptimizer is suitable for practitioners with small or even no knowledge at all of the inner workings of EAs. As long as the problem is formulated in a spreadsheet, it is very easy to set-up a number of scenarios and optimize, which is translated into significant profit or improvement in performance. All the parameters controlling the algorithms are set automatically to commonly used values. If you don't want to tune your evolutionary algorithm, you don't have to!

xlOptimizer is also ideal for researchers in any scientific field that want to use a simple yet powerful tool for optimization. All parameters controlling the behavior of each algorithm are customizable, so that experimentation can reveal the optimum set of values. Moreover, a simple, Excel-like, built-in function evaluator is included, so that the parameters can be adaptive and not fixed. For example, the optimum population size of a Genetic Algorithm (GA) depends on the complexity of the problem. This can be easily set-up into a function, that produces the population size based on e.g. the chromosome length. Finally, automation tools are included that facilitate the examination of the performance of the Algorithms. These include the capability of performing multiple independent runs, using specific or random seeds, and storing the results into appropriate folders. The log of each run contains all the necessary information regarding the evolution, and can be easily analyzed further using ASCII text editors. The core modules of xlOptimizer have been used in many papers published in peer-reviewed international scientific journals and proceedings of international conferences. For more information, please contact TechnoLogismiki.