## 1 Introduction  
After an infrastructure is opened to the public, it gradually deteriorates because of unexpected loading, temperature fluctuations, and/or numerous other environmental factors. To ensure the durability and integrity of infrastructure from these factors, it is important to monitor global structural behavior. In particular, bridges are one of the most important infrastructures. Hence, bridge behavior can provide considerable information. The intuitive global behavior of bridges not only predicts the loads applied to them, such as that in weigh-in-motion systems [1], but also evaluates the structural condition. Also, the long-term variation in the displacements provides a rationale for evaluating the degradation and aging of a structure. Although the displacements provide important information regarding intuitive global behavior, it is difficult to measure them directly. For example, to directly measure the relative displacement of a structure using instruments (such as mechanical dial gauges, linear potentiometers, and linear variable differential transducers) fixed bases are required. However,it is difficult to find fixed bases because most bridges are constructed high from the ground or cross the seas and rivers [2]. The measurement cost with the use of a supporting systems for fixed bases is sometimes very expensive. To overcome the difficulty of direct measurement of bridge displacements, various studies have been conducted to measure bridge displacements through various direct measurement approaches. [PERSON] et al. [3] researched the measurement of bridge displacement using a global positioning system (GPS). GPS shows high accuracy but has the disadvantage of being expensive. It is also affected by weather because it needs to communicate with satellites. [PERSON] et al. [4] and [PERSON] et al. [5] researched the measurement of bridge displacement using a laser doppler vibrometer (LDV) and [PERSON] et al. [2], [PERSON] et al. [6], [PERSON] [7], and [PERSON] et al. [8] researched using optic devices. Studies on the prediction of displacement from relatively easily measurable data, such as acceleration and strain, have also been researched. [PERSON] [9] and [PERSON] et al. [10] proposed a method for predicting bridge displacement from measured acceleration. Theoretically, displacements can be calculated by double integration of acceleration. However, because it is difficult to determine the initial condition (displacement, velocity) for integration in advance, it seems to be unreliable. [PERSON] et al. [11], [PERSON] et al. [12], [PERSON] et al. [13], [PERSON] et al. [14], [PERSON] et al. [15], [PERSON] et al. [16], and [PERSON] and [PERSON] [17] proposed a method for predicting displacement from strain. The curvature or modal shape of the structure can be obtained from the measured strains. Finally, the displacements can be predicted from the curvature or modal shape. Theoretically, the proposed method is feasible. However, the actual bridges in service are complex, and the load is irregular. Therefore, it is not easy to theoretically predict displacement. [PERSON] et al. [18] suggested a method for predicting the overall bridge displacements from strains using an artificial neural network (ANN). The relationship between strains and displacements is difficult to justify theoretically in the case of complex structures. However, if an ANN is utilized, the relation between strains and the displacements can be justified better than beam theory because an ANN justifies the relation based on data training rather than theory. In the previous paper [18], the feasibility of displacement prediction from strain was confirmed by numerical model. In this paper, as an extension of previous research, a method for optimizing the number and locations of strain-measurement points using a genetic algorithm (GA) is proposed to efficiently and accurately predict the displacements with fewer strains.