Keywords : Gene Expression Programming

Applying Gene Expression Programming for Solving One-Dimensional Bin-Packing Problems

Najla Akram Al-Saati

AL-Rafidain Journal of Computer Sciences and Mathematics, 2013, Volume 10, Issue 4, Pages 87-106
DOI: 10.33899/csmj.2013.163549

This work aims to study and explore the use of Gene Expression Programming (GEP) in solving on-line Bin-Packing problem. The main idea is to show how GEP can automatically find acceptable heuristic rules to solve the problem efficiently and economically. One dimensional Bin-Packing problem is considered in the course of this work with the constraint of minimizing the number of bins filled with the given pieces. Experimental Data includes instances of benchmark test data taken from Falkenauer (1996) for One-dimensional Bin-Packing Problems. Results show that GEP can be used as a very powerful and flexible tool for finding interesting compact rules suited for the problem. The impact of functions is also investigated to show how they can affect and influence the success of rates when they appear in rules. High success rates are gained with smaller population size and fewer generations compared to a previous work performed using Genetic Programming.

Automatic Test Cases Generation Using an Advanced GEP Method

Najla Akram Al-Saati; Roua Basil

AL-Rafidain Journal of Computer Sciences and Mathematics, 2012, Volume 9, Issue 1, Pages 49-65
DOI: 10.33899/csmj.2012.163687

This research aims to provide a practical work on the principle of the Extreme Programming (XP) which is a type of the Agile Software Development Methods which is used in the generation of test-cases using the design information.  The resources utilized in the design information presented here are the design diagrams generated using the Unified Modeling Language (UML), as they are considered to be the most commonly used modeling language in these days, and also the newest. These UML diagrams are used to automatically develop a set of high quality test cases which are then used to test the system’s code after being written.
The main idea of this work is based on reducing the effort of the testing stage which costs more than 50% of the resources allocated for the whole development process; this cost may include the financial cost, the cost of the resources allocated for the project, and the timeline of the project.
In this work, enhancements have been made to the concept of Gene Expression Programming to ensure the generation of high quality the test cases that are generated automatically, and a solution has been presented for the parallel paths and the loop paths problems that are found in the design.

Improving Gene Expression Programming Method

Najla Akram Al-Saati; Nidhal Al-Assady

AL-Rafidain Journal of Computer Sciences and Mathematics, 2009, Volume 6, Issue 1, Pages 81-95
DOI: 10.33899/csmj.2009.163767

In this work the algorithm of Gene Expression Programming (GEP) is investigated thoroughly and the major deficiencies are pointed out. Multiple suggestions for enhancements are introduced in this research aiming at solving the major deficiencies that were investigated. These improvements produced higher success rates and avoid the malfunctioning situations found in GEP. These deficiencies or weak points include: choosing the best parameter settings, using only one linking function, gene flattening problem, illegal operations in genes and lack of function biasing. Improvements suggested the following enhancement features: the Multi-Population feature, the Emergency Mutation feature, and the feature of ComponentBiasing. Tests are carried out using two different symbolic regression problems.