Abstract
Automatic programming, an evolutionary computing technique, forms the programs automatically and is based on higher level features that can be easily specified than normal programming languages. Genetic Programming (GP) is the first and best-known automatic programming technique that is applied to solve many practical problems. Artificial Bee Colony Programming (ABCP) is one of the latest proposed automatic programming method that combines evolutionary approach with swarm intelligence. GP is an extension version of Genetic Algorithm (GA) and ABCP is based on Artificial Bee Colony (ABC) algorithm. The main differences of these automatic programing techniques and their conventional algorithms (GA and ABC) are modeling solution. In ABC same as GA, the solutions are represented fixed code blocks. In GP and ABCP, the positions of food sources are expressed in tree structure that is composed of different combinations of terminals and functions that are specifically defined as problems. This paper presents a review on GP and ABCP and they are worked in symbolic regression, prediction and feature selection problems which are widely tackled by researchers. The results of the ABCP compared with results of GP show that this algorithm is a powerful optimization technique for structural design.
Highlights
Computer programming is the process of obtaining a program that can be executed machine to use the necessary information to perform a task
The goal of this paper is to evaluate the success of the models obtained from these automatic programming methods in symbolic regression, prediction and feature selection problems and review papers related to the problems
The experiments are run 30 times independently for Artificial Bee Colony Programming (ABCP) and Genetic Programming (GP) and the obtained results are demonstrated in Table 3 for the problems
Summary
Computer programming is the process of obtaining a program that can be executed machine to use the necessary information to perform a task. Automatic programming is a computer programming technique which automatically generates the program code [1]. It provides practical solutions for many machine learning methods such as Artificial Neural Network (ANN), Decision Trees (DT), Support Vector Machines (SVM), Genetic Programming (GP), Artificial Bee Colony Programming (ABCP). GP, most well-known automatic programming method, was developed by Koza [2]. ABCP is recently proposed automatic programming technique which is based on the Artificial Bee Colony Algorithm (ABC) [3]. The goal of this paper is to evaluate the success of the models obtained from these automatic programming methods in symbolic regression, prediction and feature selection problems and review papers related to the problems
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