## 1. Introduction

[2] The image problem of conducting objects has been a subject of considerable importance in noninvasive measurement, medical imaging, and biological application. In the past two decades, many techniques have been developed to determine the shape and locations of two-dimensional (2-D) cylindrical conducting objects. However, inverse problems of this type are difficult to solve because they are ill-posed and nonlinear. As a result, many inverse problems are reformulated as optimization problems. General speaking, two main kinds of approaches have been developed. The first is based on gradient searching schemes such as the Newton-Kantorovitch method [*Roger*, 1981; *Tobocman*, 1989; *Chiu and Kiang*, 1991], the Levenberg-Marguart algorithm [*Colton and Monk*, 1986; *Kirsch et al.*, 1988; *Hettlich*, 1994], and the successive-overrelaxation method [*Kleiman and van den Berg*, 1994]. These methods are highly dependent on the initial guess and tend to get trapped in a local extreme. In contrast, the second approach is based on the evolutionary searching schemes [*Chiu and Liu*, 1996; *Chiu and Chen*, 2000a, 2000b]. They tend to converge to the global extreme of the problem and are less dependent on the initial estimate [*Goldberg*, 1989]. Recently, researchers have applied GA together with electromagnetic solver to attack the inverse scattering problem mainly in two ways. One is surface reconstruction approach, the other is volume reconstruction approach. *Chiu and Liu* [1996] first applied the GA for the inversion of a perfectly conducing cylinder with the geometry described by a Fourier series (surface reconstruction approach), while *Takenaka et al.* [1997], *Meng et al.* [1998], and *Zhou and Ling* [2002] used the concept of local shape function to describe the conducting objects (volume reconstruction approach). Alternatively, *Zhou et al.* [2003], *Qing* [2001], and *Barkeshli et al.* [2001] used B-splines to describe the geometry of a metallic cylinder (surface reconstruction approach). Most of the conducing objects are placed in a homogeneous space, while a buried imperfect conductor is reconstructed using GA by *Chiu and Chen* [2000a, 2000b]. The scattering of a partially immersed object is discussed recently [*Ling and Ufimtsev*, 2001]. In this case, the scattering object is not immersed in a single medium, but instead is located right at the interface of two mediums, the theoretical and numerical analysis of the scattering problem become much more difficult. One has to deal with not only the usual target surface boundary condition, but also the media interface boundary condition. To our knowledge, there is still no investigation on the electromagnetic imaging of partially immersed objects. In this paper, the electromagnetic imaging of a partially immersed perfectly conducting cylinder is first reported using GA. GA exhibits several interesting features: (1) it is robust enough to reach the global extreme; (2) it is easy to incorporate a lot of a priori information; (3) the use of parallel computers to accelerate the searching procedure is possible. Although GA's are well suited in searching for the global extreme, they suffer from slow convergence as compared to a local search algorithm when they are implemented on serial computers. A natural improvement of speeding up the typical GA is to hybridize the simple GA with a local search algorithm [*Zhou et al.*, 2003]. However, the hybridization usually yields a population that is highly clustered around the local extremes. Such clustering then leads to the re-exploration of the same extreme regions, which is quite wasteful. In this paper, an improved SSGA using nonuniform probability density function (pdf) is proposed to enhance the convergence and increase the converging rate of finding the global extreme of the inverse scattering problems. It is found the improved steady state genetic algorithm can reduce the calculation time of the image problem compared with the typical steady state genetic algorithm. In section 2, the relevant theory and formulation are presented. In section 3, the details of the improved SSGA are given. Numerical results for reconstructing objects of different shapes are shown in section 4. Finally, some conclusions are drawn in section 5.