## 1 Introduction  
A wireless sensor network (WSN) is a telecommunication network that consists of a number of wireless sensor nodes. Given the limited energy and communication range of wireless sensor nodes, many previous studies have attempted to improve the quality of service (QoS) through prolonging network lifetime. Some fundamental issues have also been investigated to enhance the reliability of WSNs, such as power management [1], routing protocols [2], localization [3], medium access control (MAC) [4], and coverage control [5,6]. For mission-critical applications for WSNs, such as military surveillance [7] and object tracking [8], full sensing coverage is crucial for event detection. As a result, how well the deployed sensor nodes cover a target area is a fundamental criterion to evaluate QoS. Usually, both minimizing the energy consumption and extending the lifetime are also essential. In general, the \(k\)-coverage problem has been discussed when coverage control for WSNs is considered [5,6]. The \(k\)-coverage means that each point is within the sensing range of \(k\) or more sensor nodes. The value of \(k\) would be determined in different application-specific WSNs. For example, in [9] the authors consider the \(m\)-coverage problem in a target tracking application, where every target should be monitored by \(m\) sensor nodes. They then optimize the accuracy of target tracking and power consumption using the genetic algorithm (GA). In this paper, we consider 1-coverage problem for a WSN, where each point of interest (POI) is covered by one sensor node at least. Usually, in order to maintain a maximum sensing coverage area with the lowest power consumption, the redundant sensor nodes chosen by node-scheduling algorithms [10-12] should enter an inactive mode (sleeping mode) to reserve battery power. If one active node runs out of its energy, one or more of the inactive nodes needs to be awakened instantly to replace the dying node. These node-scheduling algorithms guarantee 100% sensing coverage as long as possible. In addition, the approach in [13] adjusts the sensing ranges of nodes dynamically to improve the sensing coverage. They utilized density control and various patterns of node placements to evaluate the performance of coverage preservation. On the other hand, the problem of selecting a minimum number of sensor nodes to cover all the required POIs belongs to the NP-Complete problem [14], also named the set covering problem (SCP) [14-16]. Some studies have applied optimization algorithms to the SCP. [PERSON] _et al_. presented an elitist non-dominated sorting genetic algorithm (NSGA-II) to find the redundant nodes and turn them off in order to conserve energy [17]. [PERSON] _et al._ employed the ant colony algorithm to solve the SCP [14]. Since the evaluation of area coverage is complicated, some studies used point coverage to approximate area coverage [18]. In this paper, we consider a SCP with a number of POIs deployed in a sensing field. In coverage control, we concern whether each POI in a sensing field that can be monitored by at least one sensor node at different time (i.e., the 1-coverage problem). Thus, the coverage control should guarantee that the original coverage is maintained after turning off redundant nodes. Memetic algorithms (MAs) are population-based heuristic search approaches for optimization problems [19]. MAs are similar to GAs, but MAs incorporate local search with GAs. Inspired by the notion of meme presented by [PERSON] [20], MAs employ one or more problem-specific heuristic searching to improve the solutions generated by GA operators, such as crossover and mutation. Hence, the performances of MAs are generally better than those of GA. Particularly, MA is a very suitable optimization algorithm for complex problems, such as the traveling salesman problem [21], the graph bi-partitioning problem [22], and binary quadratic programming [23]. MA has also been utilized to solve the minimum energy network connectivity (MENC) in WSNs [24].