By Topic

Evolutionary Computation, IEEE Transactions on

Issue 3 • Date June 2002

Filter Results

Displaying Results 1 - 7 of 7
  • Guest editorial special issue on artificial immune systems

    Publication Year: 2002 , Page(s): 225 - 226
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | PDF file iconPDF (154 KB)  
    Freely Available from IEEE
  • An immunity-based technique to characterize intrusions in computer networks

    Publication Year: 2002 , Page(s): 281 - 291
    Cited by:  Papers (92)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (468 KB) |  | HTML iconHTML  

    This paper presents a technique inspired by the negative selection mechanism of the immune system that can detect foreign patterns in the complement (nonself) space. In particular, the novel pattern detectors (in the complement space) are evolved using a genetic search, which could differentiate varying degrees of abnormality in network traffic. The paper demonstrates the usefulness of such a technique to detect a wide variety of intrusive activities on networked computers. We also used a positive characterization method based on a nearest-neighbor classification. Experiments are performed using intrusion detection data sets and tested for validation. Some results are reported along with analysis and concluding remarks View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • An artificial immune system architecture for computer security applications

    Publication Year: 2002 , Page(s): 252 - 280
    Cited by:  Papers (96)  |  Patents (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (638 KB) |  | HTML iconHTML  

    With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • From neurocomputation to immunocomputation - a model and algorithm for fluctuation-induced instability and phase transition in biological systems

    Publication Year: 2002 , Page(s): 292 - 305
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (396 KB) |  | HTML iconHTML  

    Explores bioinformatics-based modeling of immunological instabilities. We develop an algorithm for analyzing stability-instability properties of complex systems and use the developed technique to induce transitions in physical, biological and engineering systems. As a case study, we analyze the phenomena of tumor destabilization or spontaneous biological regression of a malignant focus-lymphocyte interactive system. Using stochastic noise analysis, we model high-dimensional collective oscillations of nonlinear elements and study nonautonomous systems with oscillation-induced phase transitions between lowand high-dimensional states. The associated nonlinear immunodynamical phenomenon of nonequilibrial destabilization of a malignant tumor is analyzed in terms of the Prigogine-Glansdorff (1971) stability theorem of dynamical systems theory View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Learning and optimization using the clonal selection principle

    Publication Year: 2002 , Page(s): 239 - 251
    Cited by:  Papers (618)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (470 KB)  

    The clonal selection principle is used to explain the basic features of an adaptive immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens (Ag's) are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective Ag's. This paper proposes a computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The general algorithm, named CLONALG, is derived primarily to perform machine learning and pattern recognition tasks, and then it is adapted to solve optimization problems, emphasizing multimodal and combinatorial optimization. Two versions of the algorithm are derived, their computational cost per iteration is presented, and a sensitivity analysis in relation to the user-defined parameters is given. CLONALG is also contrasted with evolutionary algorithms. Several benchmark problems are considered to evaluate the performance of CLONALG and it is also compared to a niching method for multimodal function optimization View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The immune and the chemical crossover

    Publication Year: 2002 , Page(s): 306 - 313
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (308 KB) |  | HTML iconHTML  

    Among the different mechanisms employed by evolutionary algorithms, it can be argued that recombination, or crossover, is the most original, intuitively appealing and useful in an engineering perspective. It is a simple, but natural trick to combine elements of two good individuals in the hopes of generating a better one and, in particular, by combining the elements that make these solutions good in isolation. The trick of recombination can be seen not only in genetic systems, but also in immune and chemical systems as well. This paper describes and explains these latter recombination mechanisms, first from a biological or chemical perspective, then from an engineering perspective. With regard to crossover in immune systems, several algorithmic mechanisms have already been proposed (e.g. IRM, GA-Simplex, STEP) and these are reviewed. Their basic functionality in each case is the same: new individuals are created in a zone of the search space that is shaped by the position of the current solutions, together with their fitness values. When the immune system proposes a new cell, the profile of this new candidate evidences a huge diversity, providing its adaptive capability, but this is subject to a subsequent "recruitment test" under the selective pressure of the current population of cells. With regard to crossover in chemical reactions, these can be viewed as a combination of computational graphs coupled with the distribution of the fitness values assigned to components in the graphs, as is already evidenced in particular instances of genetic algorithms and genetic programming. The benefits that these new features allow are discussed, along with other possible positive influences that come from chemistry. Finally, the paper shows how chemistry and immunology converge to this same basic message, which is in line with classical optimization techniques: exploit the information contained in the current population of solutions better before proposing a new candidate to be evaluated View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Immunotronics - novel finite-state-machine architectures with built-in self-test using self-nonself differentiation

    Publication Year: 2002 , Page(s): 227 - 238
    Cited by:  Papers (25)  |  Patents (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (397 KB) |  | HTML iconHTML  

    A novel approach to hardware fault tolerance is demonstrated that takes inspiration from the human immune system as a method of fault detection. The human immune system is a remarkable system of interacting cells and organs that protect the body from invasion and maintains reliable operation even in the presence of invading bacteria or viruses. This paper seeks to address the field of electronic hardware fault tolerance from an immunological perspective with the aim of showing how novel methods based upon the operation of the immune system can both complement and create new approaches to the development of fault detection mechanisms for reliable hardware systems. In particular, it is shown that by use of partial matching, as prevalent in biological systems, high fault coverage can be achieved with the added advantage of reducing memory requirements. The development of a generic finite-state-machine immunization procedure is discussed that allows any system that can be represented in such a manner to be "immunized" against the occurrence of faulty operation. This is demonstrated by the creation of an immunized decade counter that can detect the presence of faults in real time View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.

Aims & Scope

IEEE Transactions on Evolutionary Computation publishes archival quality original papers in evolutionary computation and related areas including nature-inspired algorithms, population-based methods, and optimization where selection and variation are integral, and hybrid systems where these paradigms are combined. Purely theoretical papers are considered as are application papers that provide general insights into these areas of computation.
 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief

Dr. Kay Chen Tan (IEEE Fellow)

Department of Electrical and Computer Engineering

National University of Singapore

Singapore 117583

Email: eletankc@nus.edu.sg

Website: http://vlab.ee.nus.edu.sg/~kctan