## 1 Introduction
In many optimization approaches for classification tasks, an objective or fitness function is mostly computed based on only the classification results. In our design approach, the multi-objective function is used based on the classification accuracy [20], overlap and compactness measurement of data structure [16] along with the constraints, _i.e._, the computational effort of feature computation and the number of selected feature [19]. In the context of our application domain, there are two commonly applied approaches of multi-objective optimization that have been proposed to solve the multi-objective problems, _i.e._, the aggregating method and Pareto-optimal methods [21]. In this paper, the aggregating method is adopted for this engineering problem for reasons of feasibility and complexity. More advanced schemes can be studied in the later stages of the work. For optimizing sensor selection, the combination and parameterization of feature computation, feature selection, and classifier, evolutionary computation (EC) techniques, in particular Genetic Algorithms (GA) [22, 23] and Particle Swarm Optimization (PSO) [24, 25] have been chosen. Both GA and PSO have been applied to optimize a variety of signal processing and feature computation as well as classification problems [12, 19, 20, 26]. However, most of these prior approaches aim at optimizing only a single operator, without regard for the the overall system design. Furthermore, according to predictions of technology roadmaps, mobile sensor nodes are expected to become constantly smaller and cheaper [27], potentially offering fast computation, constrained only by limited energy resources. This means that a designer has to achieve an efficient constrained design, _i.e._, select the combination of data processing techniques, which give low computation effort and only require small memory, but still perform well. This and a couple of other features, distinguish general sensor systems clearly from the field of industrial vision, where also several design automation or learning system designs can be observed [44, 45]. These can provide some inspiration, but are not sufficient for the challenges in the general sensor system field. Some research activities which have proposed methods and contributed to the activities for automated design of intelligent sensor systems, are briefly discussed in [28-30]. In [28], the authors focused on the sensor parameter selection for a multi-sensor array using genetic algorithms. In [29], a method to assist the designer of a sensor system in finding the optimal set of sensors for a given measurement problem was proposed. In [30], the authors proposed an algorithm based on particle swarm optimization (PSO) of model selection. This algorithm is capable of designing the best recognition system by finding a combination of pre-processing methods, feature selection, and learning algorithms from a given method pool, which provides the best recognition performance. The approach of [PERSON] _et al_. [30] does not try to optimize the selected standard models, e.g., nearest-neighbour classifier and probability neural network, with regard to resource awareness. Our goals are to contribute to the automation of intelligent sensor systems that efficiently employ a rich and increasing variety of sensor principles and electronics for an increasing number of technical fields and tasks at feasible cost. For this purpose, we propose a concept, methodology, and a framework for automated design of intelligent multi-sensor systems (ADIMSS) based on well established as well as newly evolved signal processing and computational intelligence operators to build an application-specifc system. The proposed methodology is converted to a constantly growing toolbox based on Matlab.