## 1 Introduction
Conversely, non-myopic strategies look several steps ahead, providing paths that may result better on the long run, and are usually based on graphs [10; 17; 18], random trees [14; 19; 20; 21; 22], or evolutionary algorithms [23; 24; 25]. In the framework of non-myopic strategies, a Rapidly-exploring Information Gathering (RIG) tree [20] approach was presented by [PERSON] et al. in 2014, where the vertices in RIG-tree represent a tuple of location, cost, and information. In 2019, [PERSON] et al. improved the RIG approach in [14], proposing a two-stage planning process. In the first step, the planner finds the target position providing the highest information spot under a budget constraint through Rapidly-exploring Random Tree (RRT) [26]. In the second step, it provides an optimised path to the goal. Among the category of the evolutionary algorithms are the _Genetic Algorithms_ (GA), which are commonly employed in classical cost-optimisation planning. GA are inspired by genetics and survival concepts [27; 28], ensuring flexibility in multiple scenarios, such as terrestrial [29], aerial [30], and marine [31; 32]. However, the above applications of GAs suffer from some problems, such as a high computational cost, the need to discretise the environment [33] or the necessity to have a fixed end point [34]. Furthermore, GAs are rarely used in IPP strategies for IG purposes in underwater environments. The innovation introduced by the work presented in this paper consists in the application of a GA-based IPP algorithm, renamed _Genetic Path Planner_ (GPP), with the aim of generating a path that simultaneously maximises the information gathered and the coverage of the inspected area. In particular, the analysed scenario is the monitoring of _Posidonia Oceanica_ (PO), which is an endemic low-growing Mediterranean sea grass with a high ecological value. Indeed, it is essential for the stability of coastal ecosystems, currentsand waves energy attenuation, and carbon absorption, and it is a source of food and refuge for numerous animal species. Many studies show how human activity is causing the continuous decline of this species [35], and how monitoring the ecosystems that they form is essential to plan strategies for its control and maintenance. The authors' final goal is to employ an AUV capable of finding and replanning autonomously the best path to carry out PO monitoring activities. In this paper, a GP trained on data describing the presence of PO, in probabilistic terms, has been utilised to create a model of the PO meadow extension and shape in the area of interest, through Equations (A2) and (A3) [11] (details in Appendix A.1). To obtain the dataset, used for the training of the GP, a collection of images, acquired by the AUV in previous missions over the target area, were segmented using a pre-trained _Convolutional Neural Network_ (CNN) [36]. The uncertainty of the estimate provided by the GP has then been used as input of the GPP algorithm to compute the informativeness of a given position within the inspected area. As a further step towards the final goal, the new data collected by the AUV along the planned path will be exploited to retrain the GP, and hence update the environmental model. This way, the GPP method will be able to re-plan a path according to the updated covariance map of the GP prediction. Within this work, the proposed GPP algorithm has been tested offline using GPs trained on real marine data relative to three different geographical areas covered by PO. Moreover, a comparison with other two IPP strategies has been carried out. The manuscript is organised as follows. The proposed path planner, together with the alternative planning strategies and the metrics used for the comparative analysis, are presented in Section 2. Results analysis and comparative evaluation are described in Section 3. Section 4 reports a discussion regarding the results reported in the previous Section. Finally, paper conclusions and possible future works are presented in Section 5.