Purpose – Analysis of stability of slopes has been the subject of many research works in the past decades. Prediction of stability of slopes is of great importance in many civil engineering structures including earth dams, retaining walls and trenches. There are several parameters that contribute to the stability of slopes. This paper aims to present a new approach, based on evolutionary polynomial regression (EPR), for analysis of stability of soil and rock slopes. Design/methodology/approach – EPR is a data-driven method based on evolutionary computing, aimed to search for polynomial structures representing a system. In this technique, a combination of the genetic algorithm and the least square method is used to find feasible structures and the appropriate constants for those structures. Findings – EPR models are developed and validated using results from sets of field data on the stability status of soil and rock slopes. The developed models are used to predict the factor of safety of slopes against failure for conditions not used in the model building process. The results show that the proposed approach is very effective and robust in modelling the behaviour of slopes and provides a unified approach to analysis of slope stability problems. It is also shown that the models can predict various aspects of behaviour of slopes correctly. Originality/value – In this paper a new evolutionary data mining approach is presented for the analysis of stability of soil and rock slopes. The new approach overcomes the shortcomings of the traditional and artificial neural network-based methods presented in the literature for the analysis of slopes. EPR provides a viable tool to find a structured representation of the system, which allows the user to gain additional information on how the system performs.