A Comparative Study of Automatic Programming Techniques

Sibel Arslan, Celal Öztürk


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.

Full Text:



Alan.W. Biermann. Automatic Programming: A Tutorial on Formal Methodologies, J. Symbolic Computation, pp. 119-142, 1985.


John. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, USA, 1992.


Dervis Karaboga, Celal Ozturk, Nurhan Karaboga and Beyza Gorkemli. Artificial bee colony programming for symbolic regression, Information Sciences, 209, pp. 1–15, 2012.


Hossam Faris. A Symbolic Regression Approach for Modeling the Temperature of Metal Cutting Tool, International Journal of Control and Automation. 6(4), 2013.

Panagiotis Barmpalexis, Kyriakos Kachrimanis, Athanasios Tsakonas, E. Georgarakis. Symbolic regression via genetic programming in the optimization of a controlled release pharmaceutical formulation, Chemometrics and Intelligent Laboratory Systems, 107, pp. 75–82, 2011.


Xin Li. Self-Emergence of Structures in Gene Expression Programming, Ph.D. Thesis, University of Illinois at Chicago, 2006.

Petr Musilek, Adriel Lau, Marek Reformat and Loren Wyard-Scott, Immune programming, Information Sciences, 176, pp. 972–1002, 2006.


Mariusz Boryczka, Ant colony programming: application of ant colony system to function approximation, Intelligent Systems for Automated Learning and Adaptation: Emerging Trends and Applications, pp. 248–272, 2010.


Olivier Roux, Cyril Fonlupt . Ant programming or, how to use ants for automatic programming, Proceedings of ANTS’2000, pp. 121– 129, 2000.

Shinichi Shirakawa, Shintaro Ogino, Tomoharu Nagao. T. Nagao, Dynamic ant programming for automatic construction of programs, IEEE Transactions on Electrical and Electronic Engineering, pp. 540–548, 2008.


Essam.El. Seidy. A New Particle Swarm Optimization Based Stock Market Prediction Technique, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 7, No. 4, 2016.


K.K. Manjusha, K. Sankaranayanan, P. Seena. Data Mining in Dermatological Diagnosis: A Method for Severity Prediction, International Journal of Computer Applications, (0975 – 8887) Vol. 117 – No.11, 2015.

Sana BuHamra, Nejib Smaoui, Mahmoud Gabr. The Box–Jenkins analysis and neural networks: prediction and time series modelling, Applied Mathematical Modelling, Vol: 27,pp. 805–815, 2003.


Gianluca Bontempi. Machine Learning Strategies for Time Series Prediction, Machine Learning Group, Computer Science Department Boulevard de Triomphe - CP 212, Hammamet, 2013.

Retrieved from: http://www.ulb.ac.be/di.

A. Martin, V. Aswathy, V.Prasanna Venkatesan. Framing Qualitative Bankruptcy Prediction Rules Using Ant Colony Algorithm, International Journal of Computer Applications, 0975 – 8887 ,Volume 41– No.21, 2012.


Shuzhan Wan, Shengwu. Xiong and Yi Liu, Prediction based multi-strategy differential evolution algorithm for dynamic environments, Evolutionary Computation (CEC), 2012 IEEE Congress, pp. 10-15, 2012.


Dandan .Li, Wanxin. Xue, Yilei Pei . A high-precision prediction model using Ant Colony Algorithm and neural network, International Conference on Logistics, Informatics and Service Sciences (LISS), 2015.


Hossein Etemadi, Ali Asghar Anvary Rostamy, Hossan Farajzadeh Dehkordi . A genetic programming model for bankruptcy prediction: Empirical evidence from Iran, Expert Systems with Applications, Vol: 36, pp. 3199–3207, 2009.


Pamela Dominic, David Edward Leahy, Mark J. Willis. Predicting the toxicity of chemical compounds using GPTIPS: a free open source genetic programming toolbox for MATLAB, Intelligent Control and Computer Engineering, Lecture Notes in Electrical Engineering, Vol. 70, Springer, pp. 83-93, 2011.


Yudong Zhang, Shuihua Wanga, Preetha Phillips, Genlin Ji. Binary PSO with mutation operator for feature selection using decision tree applied to spam detection, Knowledge-Based Systems, Vol: 64, pp. 22–31,2014.


Mark A. Hall , Correlation-based Feature Selection for Machine Learning, PhD Thesis, The University of Waikato, 1999.

Irene Rodriguez Lujan, Ramon Huerta, Charles Elkan, Carlos Santa Cruz. Quadratic Programming Feature Selection, Journal of Machine Learning Research, 11, pp. 1491-1516, 2010.

Jasmina Novakovıć, Perica Strbac, Dusan Bulatovıć. Toward Optimal Feature Selection Using Ranking Methods And Classification Algorithms, Yugoslav Journal of Operations Research, Vol: 21, Number 1, pp. 119-135, 2011.


Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luj´an. Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection, Journal of Machine Learning Research, Vol: 13, pp. 27-66, 2012.

Riyaz Sikora, Selwyn Piramuthu . Framework For Efficient Feature Selection In Genetic Algorithm Based Data Mining, European Journal of Operational Research, Vol:180, pp. 723–737, 2007.


Shital C. Shah, Andrew Kusiak. Data mining and genetic algorithm based gene/SNP selection, Artificial Intelligence in Medicine, Vol: 31, pp. 183—196, 2004.


Utpal Kumar Sikdar, Asif Ekbal, Sriparna Saha. Differential Evolution based Feature Selection and Classifier Ensemble for Named Entity Recognition, Proceedings of COLING 2012: Technical Papers. COLING 2012, Mumbai, December, pp. 2475–2490, 2012.


Yuanning Liu, Gang Wang, Huiling Chen, Hao Dong, X.iaodong Zhu, Sujing Wang . An Improved Particle Swarm Optimization for Feature Selection, Journal of Bionic Engineering, Vol: 8, 2011.


Bing Xue, Mengijie Zhang, Will N. Browne. Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach, IEEE Transactıons on Cybernetıcs, Vol. 43, No. 6, 2013.


Jianjun Yu, Jindan Yu, Arpit A. Almal, Saravana M.Dhanasekaran ,Debashis Ghosh, William P.Worzel, Arul M.Chinnaiyan. Feature Selection and Molecular Classification of Cancer Using Genetic Programming, Neoplasia, Vol. 9, No:4, pp. 292 – 303, 2007.


Jacques-Andre Landry, Luis Da Costa and Thomas Bernier. Discriminant Feature Selection by Genetic Programming: Towards a domain independent multi-class object detection system, Systemics Cybernetics and Informatics, Vol: 3(1), pp. 76-81, 2006.

Omer Abu-Arqub, Zaer Abo-Hammour, Shaher Mohammad Momani. Application of Continuous Genetic Algorithm for Nonlinear System of Second-Order Boundary Value Problems, Applied Mathematics& Information Sciences, 8, No.1, pp. 235-248, 2014.


Omer Abu-Arqub, Zaer Abo-Hammour. Numerical solution of systems of second-order boundary value problems using continuous genetic algorithm, Information Sciences, Vol: 279, pp. 396-415, 2014.


Riccardo Poli, William B. Langdon, Nicholas F. McPhee, John R. Koza, A Field Guide to Genetic Programming, 2016.


Zhaohui Gan, Tommy W.SChow, W.N.Chau. Clone selection programming and its application to symbolic regression, Expert Systems with Applications, Vol: 36, 2009, pp. 3996–4005, 2009.


Hajira Jabeen, Abdul Rauf Baig. Review of Classification Using Genetic Programming, International Journal of Engineering Science and Technology, Vol: 2, pp. 94-103, 2010.

Beyza Gorkemli. Study of Artificial Bee Colony Programming (ABCP) to Symbolic Regression Problems, PhD Thesis, Erciyes University, Engineering Faculty, Computer Engineering Department, 2015.

Vladimir Cherkassky, Don Gehring, Filip Mulier. Comparison of adaptive methods for function estimation from samples, IEEE Transactions on Neural Networks, Vol: 7 (4), 1996, pp. 969- 984, 1996.


Dominic P. Searson, GPTIPS 2: an open-source software platform for symbolic data mining. Chapter 22 in Handbook of Genetic Programming Applications, A.H. Gandomi et al., (Eds.), Springer, New York, NY, 2015., https:// sites.google.com/site/gptips4matlab/file-cabinet.


UCI, Machine Learning Repository, Concrete Compressive Strength Data Set. https://archive.ics.uci.edu/ml/datasets/Concrete+Compressive+Strength, Access Date: 15.10.2016.

Sibel Arslan, Celal Ozturk, Multi Hive Artificial Bee Colony Programming for high dimensional symbolic regression with feature selection, Applied Soft Computing Journal 78, 515–527, 2019.


Sibel Arslan, Celal Ozturk, Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification, Applied Sciences, 9(9), 2019.


DOI: https://doi.org/10.31449/inf.v43i2.2133

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.