Artificial Immune Systems (AIS) have long been used in the field of computer security and especially in Intrusion Detection systems. Intrusion detection based on AISs falls into two main categories. The first generation of AIS is inspired from adaptive immune reactions but, the second one which is called danger theory focuses on both adaptive and innate reactions to build a more biologically-re...

In this paper we propose a comparative study of Artificial Neural Networks (ANN) and Artificial Immune Systems. Artificial Immune Systems (AIS) represent a novel paradigm in the field of computational intelligence based on the mechanisms that allow vertebrate immune systems to face attacks from foreign agents (called antigens). Several similarities as well as differences have been shown by Dasg...

This paper proposes a new complex artificial immune system based on biological immune system. The complex data representation and complex expressions of weights are introduced into the traditional artificial immune system, in which binary or real value data representation was used. The proposed complex artificial immune system imitates the immune response mechanism closely and incorporates the ...

One of the widely used methods for classification that is a decision-making process is artificial immune systems. Artificial immune systems based on natural immunity system can be successfully applied for classification, optimization, recognition, and learning in real-world problems. In this study, a reinforcement learning based artificial immune classifier is proposed as a new approach. This a...

The adaptable lymphocyte hypothesis is identified as a possible source of inspiration for artificial immune systems. Based on a number of qualitative investigations we identify some properties of a theoretical system (the tunable activation threshold model and excitability) that could be applicable in an engineering domain. An example is shown of how we could exploit these properties.

Artificial Immune Systems (AIS) have been proposed to solve binary classification problems; distinguishing between instances of self and of non-self. For any classification system such as AIS, the choice of classifier representation used impacts substantially on the kind of classification problem that can be handled. Despite this, most classication systems such as AIS make use of only one repre...

We propose an efficient method of extracting knowledge when scheduling parallel programs onto processors using an artificial immune system (AIS). We consider programs defined by Directed Acyclic Graphs (DAGs). Our approach reorders the nodes of the program according to the optimal execution order on one processor. The system works in either learning or production mode. In the learning mode we u...

The paper concentrates on analyzing associative properties of Artificial Immune Systems, especially on immunological memory, which is a member of a class of sparse and distributed associative memories [18]. This class of memories derives its associative and robust nature by sparsely sampling the input space and distributing the data among many independent agents [16]. Immunological memory is on...