ARTIFICIAL IMMUNE SYSTEMS (AIS)

Few words on AIS

During the last decade, the field of Artificial Immune System (AIS) is progressing slowly and steadily as a branch of Computational Intelligence (CI)

.There has been increasing interest in the development of computational

models inspired by several immunological principles. In particular, some

are building models mimicking the mechanisms in the biological immune system (BIS) tobetter understand its natural processes and simulate its dynamical behavior in the presenceof antigens/pathogens. Most of the AIS models, however, emphasize designing artifacts–

computational algorithms, techniques using simplified models of various

immunological processes and functionalities. Like other biologically-inspired techniques,such as artificial neural networks, genetic algorithms, and cellular automata, AISs also try to extract ideas from the BIS in order to develop computational tools for solving science and engineering problems. Although still relativelyyoung, the Artificial Immune System (AIS) is emerging as an

active and attractive field involving models, techniques andapplications of greater diversity.

Our body’s immune system is a perfect example of a learning system.It is able to distinguish between good cells and potentially harmful ones.

Artificial Immunes Systems (AISs) are learning and problem solvers based on our own immune systems.

• AISs have been used to solve a wide variety of problems including:

–       Computer Security,

–       Pattern Recognition,

–       Mortgage Fraud Detection,

–       Aircraft control,

–       Etc.

• A typical AIS is composed of three type of detectors:

–       Immature,

–       Mature,

–       Memory

• Detectors match instances (training and/or test) via a matching rule.

–       A matching rule that is too general will allow a detector to match many instances;

–       A matching rule that is too specific will cause the detector to match few instances.

• An AIS evolves a population (detector set) over time.

–       Some immature detectors will be promoted to mature detectors (some immature detectors will die)

–       Some mature detectors will be promoted to be memory detectors while other mature detectors will die.

–       Some memory detectors may die due to:

• Changes in the problem
• Old age.

Artificial Immune Systems Immature Detectors

• Consider a problem where one must categorize an input instance as a member of one of two categories. Let the categories be self and non-self.
• Immature detectors are randomly generated and checked to see if they match any instances (in the training set) that are self.
• Any immature detectors that match a self instance die (are removed from the detector population) and are replaced with a new, randomly generated immature detector.
• Immature detectors that fail to match a  timmature time (typically measured in instances) in a row are promoted to being mature detectors.

The above process is referred to as Negative Selection.

Artificial Immune Systems Immature Detectors

• Once a detector becomes a mature detector is will usually match instances.
• Mature detectors are allow tmature amount of time to detect (or match) mmature non-self instances.
• tmature represents the learning phase of a detector.
• Mature detectors that fail the match the required number of anomalies, mmature, within the specified amount of time, tmature, die an are replaced with a randomly generated immature detector. Otherwise the mature detector becomes a memory detector.

Artificial Immune Systems

• How will increasing mmature affect the performance of an AIS in terms of False Positives?
• What effects could it have on:

–       The immature detector sub-population,

–       The mature detector sub-population, and

–       The memory detector sub-population?

• What effect would the values assigned to timmature and tmature have on the performance of an AIS.
• What effects could they have on:

–       The immature detector sub-population,

–       The mature detector sub-population, and

–       The memory detector sub-population?

• The representation for the detectors of an AIS may be:

–       Binary-Coded, or

–       Real-Coded

• For Binary-Coded Representations, an r-contiguous bits matching rule can be used,
• For Real-Coded Representations, an any-r intervals matching rule can be used.
• Consider the following AIS:

Detector-1: <00101110>

Detector-2: <11001010>

Detector-3: <10010010>

• And the following input:

Input:              <10101010>

• Using the r-contiguous bits matching rule, which detectors match the input if:
• r = 1, 2, 3, 4, and 8

Future Directions

This technical report started with an instructive introduction to the immune system, followed by the development of several engineering tools capable of solving complex problems in many areas of research, like computation, mathematics, engineering and others. from this we can expect that this text reinforces the importance of studying and using biological systems, in particular the immune system, as powerful sources of inspiration for the development of alternative solutions to problems that still can not be resolved by conventional engineering techniques.

The immune engineering was introduced as a branch of the AIS that uses ideas gleaned from

immunology in order to generate dedicated solutions based solely upon the representation of the

problems. A set of potential candidate solutions can be defined, together with a function to measure their affinity to the environment. The AIS might embody all immunologically inspired strategies including hybrid immune systems, immune engineering tools and their applications, immunogenetic approaches, and others. In addition, the shape-space formalism was proved to present a reasonable representation (vocabulary) for the development of immune engineering tools.

It was demonstrated, through the development of several algorithms, that with simple systemic

views of the immune system, we can manage to engineer different computational techniques. The 86 applications used as illustration, focused on the generation of diversity, pattern recognition and classification, function approximation, multi-modal and combination optimization. However, other types of examples could have been used to test the proposed algorithms, like control, system identification, computer security, sensor-based diagnosis, scheduling, fault detection, data analysis.

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