## 1 Introduction  
In control theory research, is common to rely on software tools for explore, evaluate, an tune control strategies. These tasks are time consuming, even with software tools of increasing performance and complexity. If in addition, researchers want to evaluate different control alternatives and variations they have to perform hundreds, even thousand o simulations; consequently they need to dedicate long periods of time on simulation tasks. This paper is aimed to reduce such time by providing a software tool to automate the task of controller exploration, evaluation and tuning. Recently, evolutionary algorithms (EA) for automatic controller generation has been proposed [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]. These algorithms begin by creating a set of elements, called the initial generation. From the initial generation, successive generations are derived by creating a new generation from the previous one. In control applications of EA, each element of every generation is a controller, that is, a mathematical expression of system state. Elements of initial generation is created by a random combination of parameters and functions of the error and state variables [17, 18, 19, 12]. Roughly speaking, to derive a next generation from the current one, the following steps are carry-out: (a) performance of each element is evaluated using a cost function; (b) fitness is assigned to each element according to its performance; (c) some elements of the current generation are selected based on its fitness; (d) selected elements undergo a process called genetic operations to produce other elements. The process of selecting elements and apply a genetic operation is repeated till a complete new generation is created. EA have been applied to a great variety of control engineering problems, from tuning of classic controllers, to generation of free-form controllers. Just to name a few, in [7; 20; 21] genetic algorithms to tune PID controllers for an electro-hydraulic valve; continuous stirred tank reactor; and 3 DOF robot system are proposed, respectively. In these works, each element of a generation codes a PID controller; the algorithm ends when a good enough controller is obtained. Authors of [20] compare traditional PID tuning with several metaheuristics algorithms; they conclude that development of metaheuristic algorithms for effective optimization and controller tuning is still remains an active research area for academicians and industrial practitioners. In [7; 20; 21], structure of controllers do not change along the algorithm execution, hence the algorithm is aimed to search the best combination of controller parameters. On the other hand, EA proposed in [8; 19; 22] can create and evaluate a great number of controllers; however the possible number of controllers for any dynamical system is huge. The search space can be reduced by restricting the kind and number of functions allowed in the creation of controller. However, it would be convenient that in addition to this form of restriction, the researcher could specify the kind of controllers to search. Restricting the kind of controllers the algorithm is able to produce is advantageous because when a researchers deal with a problem, usually they have previous knowledge about the system; such knowledge can come from heuristics, mathematical analysis, experience, close examination, etc. In any case, a researcher generally have an idea of advisable controller techniques to solve the problem at hand. Furthermore is would be advantageous to be able to test some variations on the controller structure. It would be very useful to have a tool able to search controllers with some variations around a pre-specified controller structure. In this manner, the knowledge of the engineer would be present through the pre-defined structure but at the same time, variations around it would be allowed. In this paper such a tool is provided. The tool has as its main part an EA that only generates controllers of a pre-specified morphology.