This chapter describes recent advances in genetic programming of machine code. Evolutionary program induction using binary machine code is the fastest known Genetic Programming method. It is, in addition, the most well studied Genetic Programming system that uses a linear genome. Evolutionary program induction using binary machine code was originally referred to as Compiling Genetic Programming...

This paper explores the use of the optimization procedures in SAS/OR software with application to the contemporary logistics distribution network design using an integrated multiple criteria decision making approach. Unlike the traditional optimization techniques, the proposed approach, combining analytic hierarchy process (AHP) and goal programming (GP), considers both quantitative and qualita...

supplying required human resource is one of the strategic goals of an organization. therefore, organizations plan and predict the required human resources in order to prevent this problem. present study using practical approach seeks to assist organizations to achieve their various goals in human resources planning. the designed model in this study clarifies vague, inaccurate, and fuzzy express...

Genetic Programming (GP) is an automated method for creating computer programs starting from a high-level description of the problem to be solved. Many variants of GP have been proposed in the recent years. In this paper we are reviewing the main GP variants with linear representation. Namely, Linear Genetic Programming, Gene Expression Programming, Multi Expression Programming, Grammatical Evo...

There are various automatic programming models inspired by evolutionary computation techniques. Due to the importance of devising an automatic mechanism to explore the complicated search space of mathematical problems where numerical methods fails, evolutionary computations are widely studied and applied to solve real world problems. One of the famous algorithm in optimization problem is shuffl...

Extracting representative feature sets from raw signals is crucial in Prognostics and Health Management (PHM) for components’ behavior understanding. The literature proposes various methods, including signal processing the time, frequency, time–frequency domains, selection, unsupervised learning. An emerging task data science Feature Construction (FC), which has advantages of both selection In ...