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TUTORIAL ARTIFICIAL AMMUNE SYSTEM
1. Discuss how AIS use the immune system as metaphor. Consider the pattern recognition, memory, learning learning capability and anomaly in your answer. Artificial Immune Systems (AIS) include algorithms and systems that use the human immune system as inspiration. The human immune system is a robust, decentralised, error tolerant and adaptive system. Such properties are highly desirable for the development of novel computer systems. It also can characterize complex systems and contribute to the growth of complexity science. The main features of the AIS include pattern recognition, anomaly, learning capability, memory, and its distributive nature gives rich metaphors for its artificial counterpart. counterpart.
2. Discuss each of the component in AIS framework. Draw the framework.
SOLUTION
ALGORITHM AFFINITY REPRESENTATION
APPLICATION
AFFINITY Computationally, the degree of interaction of an antibody-antigen or antibodyantibody can be evaluated by a distance or affinity measure.
Shape space (binary, integer, real-valued, symbolic)
used for modelling antibody and antigen
determine a measure to calculate affinity
ALGORITHM
Bone Marrow Model
Clonal Selection
Negative Selection
Network Models
APPLICATION
Robotics
Behaviour Arbitration
Collective Behaviour
3. Briefly explain how negative selection algorithm diagnose the cancer disease. You can refer to article/youtube. Negative selection is a mechanism that protects the body against the autoreactive lymphocytes. It uses the ability of the immune system to detect unknown antigens without affecting the self cells. There are two processes which are the recognition of the shape of the antigen and selecting the antibody specific to it. The idea is that on ly antibodies capable of recognizing the antigen are activated for proliferation (cloning + mutation).
4. Discuss three (3) advantages of the AIS employment in job scheduling.
AIS technique in Job Scheduling always gives a non-delay schedule. It has good solutions which can identified at an early stage in the process by being on a constant avoidance of constraints, which in turn speed up the process of finding good solutions.
The computation time is much less than traditional technique.
The entire schedule is built up in this manner and the minimum make span is calculated for each schedule. A solution with the least make span within the pool of potential solutions is considered to be the best in that pool.
AIS technique produced robust schedule. AIS is able to give the flexibility and robustness to tasks and set their execution dates. Sometimes a schedule simply consists of a sequence of tasks by machine with a simple rule for calculating task start times. The inherent feature of robustness of AIS is demonstrated by the near-optimum solutions in all cases.
