## 1 Introduction  
A reliable and complete knowledge of current plant state is essential for plant monitoring, regulatory and supervisory control, real time optimization, planning and scheduling, etc. The quality and availability of variable estimates strongly depend on the sensor network (SN) installed in the process and the data reconciliation packages used to enhance the precision of estimates during plant operation. The problem of selecting a set of variables to be measured, which is optimal with respect to some specified criteria and simultaneously provides the quantity and quality of information required from the process, is called the sensor network design problem (SNDP). Different types of instrument arrangements arise depending on the number and location of the selected measurements. A minimum-number SN contains the smallest amount of instruments that allow the estimation of all unmeasured variables. If more sensors are used than the minimum required to satisfy the aforementioned condition, a so-called redundant SN is obtained. The quantity of sensors contained in both types of arrangements is known in advance to problem resolution. In practice it is necessary to satisfy constraints only on a subset of key measured or unmeasured variable estimates. In this case a general SN is designed without knowing in advance the cardinality of the optimal sensor set. As only a subset of variables are of real practical interest, the optimal selection of measurements for general SNs is a powerful tool for the design of large-scale plants. Diverse criteria have been proposed for the optimal selection of sensor structures, such as the minimization of cost while ensuring quality constraints (precision, reliability, estimability) on variable estimates, the maximization of the minimum variable-estimate availability subject to cost constraints, the minimization of the global error of variable estimates for a fixed instrumentation-project budget, etc. In SND the important decision to be made with regard to each stream variable is whether to measure it or not. To mathematically formulate these decisions, binary variables are employed which indicate the presence or absence of sensors. The problem is usually multimodal and involves many binary variables, therefore a huge combinatorial optimization problem subject to constraints should be solved. At first meta-heuristics based on Genetic Algorithms (GAs) [1-2] were proposed to deal with the design of sensor structures. In this regard, [PERSON] et al. [3] developed an evolutionary procedure devoted to the selection of flowmeters for nonredundant SNs. It optimizes single criteria such as cost, reliability or estimation accuracy based on concepts from graph theory. [PERSON] et al. [4] dealt with the optimal design of non-redundant structures for linear processes that ensure the observability of all unmeasured variables while optimizing single or multiple criteria. The solution procedure was developed using a GA whose operators were modified based on linear algebra concepts. Furthermore, [PERSON] and [PERSON] [5] presented an evolutionary approach for the design of linear redundant SNs that maximize the reliability of variable estimates. In previous references, the quantity of sensors to be installed in the network is fixed before the run of the optimization procedure. Regarding the design of general SNs, characterized by the fact that the optimal number of sensors is unknown in advance, [PERSON] et al. [6] presented the design of maximum-availability networks subject to cost and precision constraints, but they solved the problem for a small size network using the classic GA. [PERSON] and [PERSON] [7] presented two ways of parallelizing the GA, namely the global parallelization and the distributed GA, to reduce the solution time. They concluded that both techniques allow reducing the elapsed time but the second one is more efficient. Also [PERSON] et al. [8] applied GAs to solve the design and retrofit of reliable SNs.