Showing papers on "Artificial immune system published in 1996"

Learning using an artificial immune system

Abstract: In this paper we describe an artificial immune system (AIS) which is based upon models of the natural immune system. This natural system is an example of an evolutionary learning mechanism which possesses a content addressable memory and the ability to «forget» little-used information. It is also an example of an adaptive non-linear network in which control is decentralized and problem processing is efficient and effective. As such, the immune system has the potential to offer novel problem solving methods. The AIS is an example of a system developed around the current understanding of the immune system. It illustrates how an artificial immune system can capture the basic elements of the immune system and exhibit some of its chief characteristics. We illustrate the potential of the AIS on a simple pattern recognition problem. We then apply the AIS to a real-world problem: the recognition of promoters in DNA sequences. The results obtained are consistent with other appproaches, such as neural networks and Quinlan's ID3 and are better than the nearest neighbour algorithm. The primary advantages of the AIS are that it only requires positive examples, and the patterns it has learnt can be explicitly examined. In addition, because it is self-organizing, it does not require effort to optimize any system parameters.

Active noise control by an immune algorithm: adaptation in immune system as an evolution

Abstract: A new information processing architecture is extracted from immune systems. By focusing on informational features of the immune system (i.e. specificity, diversity, tolerance, and memory), an immune algorithm is proposed. The algorithm proceeds in three steps: diversity generation, establishment of self-tolerance, and memorizing non-self. An agent-based architecture based on the local memory hypothesis and a network-based architecture based on the network hypothesis are discussed. An agent-based architecture is elaborated with an application to control systems where the knowledge about disturbances is not available. An adaptive disturbance neutralizer is formalized and simulated for a simple plant.

An immune algorithm for multiagent: application to adaptive noise neutralization

Abstract: A new information processing architecture is extracted from the immune system. By focusing on informational features of the immune system (i.e. specificity, diversity, tolerance, and memory), an immune algorithm is proposed. The algorithm proceeds in three steps: diversity generation, establishment of self-tolerance, and memorizing non-self. The algorithm may be used typically to model the system by distributed agents where the system (the self) as well as the environment (the non-self) are unknown or cannot be modeled. Agent-based architecture based on the local memory hypothesis and network-based architecture based on the network hypothesis are discussed. Agent-based architecture is elaborated with the application to an adaptive system where the knowledge about environment is not available. Adaptive noise neutralization is formalized and simulated for a simple plant.

An associative memory based on the immune networks

Abstract: In this paper we propose the use of immune network model for designing associative memories. Particularly, we show how the model of an immune system can be applied to the associative memorization problem, by defining an appropriate type of recruitment mechanism. Simulation results are shown to compare favourably with those produced from an other well-known model.

Immunizing against fraud

Abstract: Immunizing financial organizations against loan and mortgage fraud is a non-trivial problem It involves the non-trivial identification of valid, novel, potentially useful, and ultimately understandable patterns in very large amounts of data The critical issue is that we are trying to extract knowledge from data Depending on the techniques used, the identified knowledge can then be used to: identify fraud in new applications; explain existing fraud; enable logical (as opposed to “graphical”) data visualization to aid humans in discovering deeper fraud patterns An important issue relating to fraud is that as databases grow, fraud identification directly from their contents by humans becomes more and more difficult A number of approaches have been developed to aid the human in their task One such approach involves the use of AI techniques such as neural networks and machine induction In this paper we describe a system based on metaphors taken from the human immune system This system (referred to as the Artificial Immune System) is a general purpose computer-based learning system which has been applied to applications such as loan and mortgage fraud (4 pages)