Occupied with work, alhamdulillah chapter 1 is finished, sekang nih buleh la sambung chapter 2 yang banyak tuh!!!! Was planning to submit tomorrow tapi tak sure la buleh siap ke tak….:( doa je banyak2 ni…:)
Part of the chapter is here :
Biological inspired computing, which is the use of biology or biological processes as metaphor or inspiration in developing new computing technologies and new areas of computer science has become a motivation for researchers in many disciplines, including computer science, biology, mathematics and physics, to investigate biological systems or to provide inspiration for solving different types of problems that they
encounter. However, there are many immune processes that are not well understood, and in addition, there is little agreement amongst many immunologists regarding many of the key immune principles leading to
a lack of clearness as to the functioning of many immune processes. This leaves the researcher to decide which aspects of immunological theory to take inspiration from, as there is wide range of choices.
With computer modelling and simulation techniques, a better understanding of biological systems can be achieved, enriching the development of engineering techniques inspired by the systems. This thesis outline reports on work in progress from the initial work done in (Ismail, 2008) and (Ismail,2009). This introductory chapter is divided into two sections. In Section 1.1, we describe why we would like to model biological systems in accordance to our need in proposing an AIS algorithm that follows a principled design methodology that can provide experimental evidence for, or against, the assertions made by immunologists in their theories as well as AIS practitioner. In Section 1.2 outlines the structure and major conclusion for each chapter in this thesis outline.
Complexity is described by (Goldenfeld & Kadanoff, 1999) as
‘systems that have structure with variations’
A complex system is interesting because it is highly structured. Nature can produce complex structures even in simple situations. Thus, a living organism is complex because it has many different working parts, each formed by variations in the working out of the same genetic coding (Goldenfeld & Kadanoff, 1999). Complex systems is an approach to science that studies how relationships between parts give rise to the collective behaviors of a system and how the system interacts and forms relationships with its environment. Complex systems is therefore often used as a broad term encompassing a research approach to problems in many diverse disciplines including artiﬁcial life, chemistry, computer science, economics, evolutionary computation, biology, neuroscience, physics, psychology and sociology. Immune systems are complex
systems as it consists of networks or organs containing cells that recognize foreign substances in the body and destroy them. It protects the body against pathogens or infectious agents such as viruses, bacteria and other parasites. The components of immune systems exhibit many of the hallmarks of the complex systems, which is studied through out this research.
Based on the preceding discussion, we further turns our attention on how to understand complexity, which we highlight the needs of modelling and simulation in Section 1.1.1. We end this section by relating again our work in immune systems leading to the development of AIS algorithm in Section 1.1.2
1.1.1 Understanding complexity
In his article, Goldenfeld & Kadanoff (1999) describes how we can understand complexity. The author mentioned, which is quoted as below:
‘To extract physical knowledge from a complex system, one must focus on the right level of description. There are three modes of investigation of systems like this: experimental, computational, and theoretical’
Goldenfeld & Kadanoff (1999) explains further that experiment is best for exploration, since experimental techniques with the combination of human eye can scan large data efﬁciently. Meanwhile, computer simulation is often used to check our understanding of a particular physical process or situation. It means that we can exploit the process or situation, by designing the most convenient minimal model. Even though the simulations are so slow that they may not be able to reach a regime which will enable us to safely extrapolate to large systems, which will lead to a wrong answer, Goldenfeld & Kadanoff (1999) suggests that we should model at the macro level, use large time steps and a large systems. Further to Goldenfeld & Kadanoff (1999) discussion, he also mentions that modellers need to use the right level of description to catch the phenomena of interest, which is applies to the theoretical work aimed at understanding complex systems. In terms of modelling complex systems, Goldenfeld & Kadanoff (1999) emphasise on the notion of tractable closure schemes or complicated free-ﬁeld theories that always yield a successful description of the small-scale structure, but likely to be irrelevant for the large-scale features. To achieve success, one should therefore use more phenomenological and aggregated description, aimed speciﬁcally at the higher level, which need to be created in a tractable way and derive from and derive from crude modelling approaches (Goldenfeld & Kadanoff, 1999).
So every good model starts from a question. The modeller should always pick the right level of detail to answer the questions that the have in mind before starts modelling complex systems. So why is this research tries to simulate complex system? The reasons for simulating complex system, such as biological systems, are numerous. Biological systems usually involve large population of entities acting simultaneously, and by having simulations and models that represents the biological system allow the modeller to have a greater understanding of the system. Thus, by performing experiment, we anticipate to better understand of its behaviour as well as its function. But why we would like to understand the systems better? This question is tackled in Section 1.1.2 where we highlight the needs of modelling in designing an AIS algorithm. One way to examine what may be happening in complex systems is through the use of computer simulations. Two free software programs (list of programs are attached as Appendix B), StarLogo 1 and NetLogo …..can be read further in the original Thesis Outline