# Target State Optimization: Drivability Improvement for Vehicles with Dual Clutch Transmissions
## 1 Introduction
[7], the evaluation difference threshold (EDT) is proposed. The EDT describes how much the stimulus intensity must be changed, in order to generate a variation of an evaluation (the significantly varying evaluation is determined within statistic tests). The EDT is generally larger than the JND. There are hardly any studies investigating the EDT for the launch behavior for determining the variation of an evaluation according to sportiness, comfort, and jerkiness. The identified EDTs of this study offer an orientation for choosing reference values for the automated calibration of the launch behavior with different optimization algorithms. Genetic algorithms (GA) are a common approach to solve multi-objective optimization problems like the calibration of the parameters of the transmission control unit (TCU). In general, GA are inspired by genetic evolution in nature and usually an initial population is determined randomly [13]. Each population consists of several individuals. For each individual, a fitness value is assigned based on the behavior the individual performs in the desired environment (how it fits to the optimization objectives) [14]. GA also typically consists of the steps: selection, mutation (spontaneous altered genetic [15]), and recombination (inheriting the shared genetic of parents to a child [16]). Based on the fitness value, the individuals are selected for mutation and recombination [17]. After mutation and recombination, new individuals are part of the following generation. In using GA, the population size and the number of generations have to be defined [13]. The GA NSGA-II algorithm from [PERSON] et al. [13] is a well-tested algorithm for solving such kinds of multi-objective optimization problems, and has been applied in a wide range of optimization problems [2; 18; 2; 19; 20; 21]. [PERSON] [20; 21] used this algorithm in the scope of parameter optimization for optimal shift control of dual clutch transmissions. The shift control has been optimized through the minimization of the jerk, and the minimization of the derivative of the jerk, as optimization objectives. Additionally, the optimization of the launch process with the same objectives as [PERSON] [20; 21] have been applied by [PERSON] et al. [2] for DCT. [PERSON] et al. [2] additionally introduced another optimization objective by measuring the time from start of the launch (acceleration pedal unequal zero) to synchronization (clutch slip speed equal zero). [PERSON] et al. [23] optimized the vehicle launch using the differential evolution algorithm for vehicles with DCT. For optimization, the clutch closing time and a scaling factor for clutch slip speed have been applied as objectives. GA have also been applied in other fields, like the optimization of calibration parameters of the engine control unit (ECU). To optimize ECU calibration parameters, a novel GA has been invented based on existing GA. With a special design of experiments, an initial population is identified for reducing the evaluation time. For this aim, a gray-box model based on a neural network is used to model the engine [24]. Additionally, [PERSON] [25] and [PERSON] et al. [26] applied GA to optimize driving behavior by improving the clutch engagement of a transmission. GA, however, have some disadvantages, such as the long computation time [27] and the potential of remaining in a local minimum [28]. The parameter population size (number of individuals per generation), for example, directly influences the computation time and affects the quality of the evaluation [29]. The quality of the solution of a GA is also influenced by choosing the right parameters for crossover and mutation [30]. As deep learning has gained attention since mastering challenging tasks like speech recognition and object recognition [31], reinforcement learning (RL) has also evolved. In RL, information about the problem is obtained by the interaction of an agent with an environment [32], such as the famous example of a Q-learning algorithm playing Atari Games [33].