Proceedings of 2010 IEEE Student Conference on Research and Development (SCOReD 2010), 13 - 14 Dec 2010, Putrajaya, Malaysia Hybrid Artifcial Immune System-Genetic Algorithm optimization based on mathematical test fnctions Mohammed Obaid Ali I , S. P. Koh l ,K. H. Chong \ David F. w. Yap 2 , Department of Electronics and Communication Engineering I Universiti Tenaga Nasional (UNITEN) 43009 Kajang, Selangor Darul Ehsan, Malaysia 2 Universiti Teknikal Malaysia Melaka(UTeM) Hang Tuah Jaya,76100 Durian Tunggal, Melaka, Malaysia I {engin mohammed2020@ahoo.com}, I {johnnykoh,chongkh@uniten.edu.my} ,z {david.yap@utem.edu.my} Abstract- This paper demonstrates a hybrid between two optimization methods that are Artifcial Immune System (AIS) and Genetic Algorithm (GA). The capability of overcoming the shortcomings of individual algorithms without losing their advantages makes the hybrid techniques superior to the stand-alone ones based on the dominant purpose of hybridization. The improvement of the results that enable to get it if GA and AIS work separately is the main objective of this hybrid. The hybrid includes two processes; frstly, AIS is the attraction among the researchers as the algorithm. This enables it to develop local searching ability and efciency yet the convergence rate for AIS is preferably not precise compared to the GA. Secondly, a Genetic Algorithm is typically initializing population randomly. The last generation of AIS will be the input to the next process of the hybrid which is the GA in this hybrid AIS-GA. Hybrid makes GA enters the stage of standard solutions more rapidly and more accurate compared with GA initialized population at random. To diferentiate between the results in terms of achieving the minimum value for these fnctions, eight mathematical test fnctions are being used to make comparison. Kewords: Hybrid, Artifcial Immune System (AIS), Genetic Algorithm (GA) optimization mathematical test fnctions. I. INTRODUCTION Generally, optimization theory is the core of mathematical results and numerical methods. Its fnction is for searching and discovering the best resolution fom a collection of options without having to accurately enumerate and evaluate all likely options [1]. The process of optimization lies at the beginning of engineering itself. The usual fnction of an engineer is to improve designs and create new ones with more effcient and less expensive systems as well as to elaborate plans and procedures for the existing systems [2]. The competency of optimization techniques to identif the fnest case without having to test all potential cases that comes through the use of a simple level of mathematics and at the cost of executing iterative numerical calculations using acutely defned logical process or algorithms applied on computing machines [3]. Adeptness with basic-matrix manipulation, linear algebra and calculus are needed to develop the optimization methodology [4]. It is essential to characterize the breaches of the engineering system to be optimized to apply the mathematical results and numerical 978-1-4244-8648-9/101$26.00 ©2010 IEEE 256 methods of optimization theory to defnite engineering problems[5,6,7]. II. ARTIFICIAL IMMUNE SYSTEM People ofen t to nature whenever seeking new ideas to solve computational problems that has become much complex. The vertebrae immune system has been greatly attended to as it is a good potential source of inspiration. It is thought of as the possibility to glean different insights and alterative solutions, compared to other biological-based methods. Due to a wide variety of problems ranging fom optimization, fault tolerance, data mining, bioinformatics and robotic systems being opposed with the development of solutions, the feld of Artifcial Immune System (AIS) has become popular with its high distribution. It is also highly adaptive, self-organizing in nature, capable of maintaining memories of past encounters while continually able to lea about upcoming encounters that has never been approached. From over the past few years, AIS-based works span fom theoretical modeling and simulation to more variety of application. Interest in the AIS feld has been increasing among many of new works inclusive in this feld of research today [8, 9, 10]. The artifcial immune system (AIS) applies a leaing method infuenced by the human immune system. The human immune system is an amazing natural defense mechanism that defends and leas about foreign substances [11]. The AIS objective using idea based on immunology for the growth of systems are capable of doing numerous tasks in various areas of research [12, 13]. The detection of harmfl foreign element which is called pathogens that attacks us is the main task of our immune system. Our immune system defends us fom the pathogens. Bacteria and viruses are the examples of such pathogens. An atigen is a molecule that can be recognized by our immune system. Such molecule triggers a specifc reaction fom our immune system. An important role is played by the Lymphocytes which are a special type of cells in our immune system. There are two types of lymphocytes exist which is the B cells or B lymphocytes and T cells or T lymphocytes. When detecting the antigen, the B cells that best match the antigens are cloned.