## 2 GA-Based Works on WSNs
### GA-Based Optimization in WSNs  
Genetic algorithms have been applied and to solve many engineering problems with GAs' excellent search abilities, and the use of GAs in wireless sensor network design has been proved to be successful by a number of works. The most common use of GAs is how to achieve an energy-aware network under specific routing conditions, which includes the finding of proper cluster heads for data  
\begin{table}
\begin{tabular}{l} \hline \hline
**Advantages of GAs** \\ \hline \(\surd\) Parallelism, efficiency, reliability, easily modified for different problems \\ \(\surd\) Large and wide solution space searching ability \\ \(\surd\) Non-knowledge based optimization process \\ \(\surd\) Use of fitness function for evaluation \\ \(\surd\) Easy to discover global optimum, avoid trapping in local optima \\ \(\surd\) Capable of multi-objective optimization and can return a suite of potential solutions \\ \(\surd\) Good choice for large-scale/wide variety optimization problems \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages of Genetic Algorithms (GAs) when compared to conventional methods.  
Figure 1: Brief flowchart of genetic algorithm.  
aggregation in the network; the selection of the optimal path to reduce hops (and thus transmission energy); and at the same time the reduction of channel contention. Such efforts for energy-efficiency can be found in [24, 25, 26]. In addition, researchers in [27] employed GAs in the multiple QoS (quality of service) parameter problem trying to determine near-optimal multicast routes for Mobile _ad hoc_ network (MANET).  
In [28], a GA-based methodology is proposed for adaptive wireless sensor network design. A fitness function that incorporates seven design parameters from the aspects of the specific application, network connectivity and energy consumption is used for the evaluation, so that during the optimization process many requirements (such as the status of the sensor nodes, the selection of the cluster heads, as well as the distance between nodes) are taken into account for the design of a reliable and energy-aware wireless sensor network.  
In recent years, the use of GAs to find the optimal placement of wireless sensor network nodes before real deployment has gained wide attention from many research groups and academic institutions. However, in the placement problem, the optimization process is required to tune multiple conflicting objectives, e.g., reducing the energy consumption while achieving large coverage and robust connectivity, so Multi-Objective Genetic Algorithms (MOGA) are adopted in [29, 30, 31], aiming to find a deployed network arrangement to maximize area coverage, minimize energy cost (maximize lifetime), and maintain good connectivity.  
The remainder of the paper is organized as follows. Section 3 presents the motivation of proposing the optimization framework. Section 4 illustrates the detailed design method and implementation of the proposed framework including the description of the architecture, related performance metrics, weighted sum cost function based optimization, multi-scenario/multi-objective based optimization, the settings of the framework via command lines, and a MATLAB based GUI as well as the generic use of the framework. Section 5 is the experiment part used to verify the effectiveness and availability of the framework. Finally, we conclude in Section 6.