A Review of Artificial Immune System Based Security Frameworks for MANET <s> Clonal Selection <s> This paper presents a comparative study of two important Clonal Selection Algorithms (CSAs): CLONALG and opt-IA. To deeply understand the performance of both algorithms, we deal with four different classes of problems: toy problems (one-counting and trap functions), pattern recognition, numerical optimization problems and NP-complete problem (the 2D HP model for protein structure prediction problem). Two possible versions of CLONALG have been implemented and tested. The experimental results show a global better performance of opt-IA with respect to CLONALG. Considering the results obtained, we can claim that CSAs represent a new class of Evolutionary Algorithms for effectively performing searching, learning and optimization tasks. <s> BIB001 </s> A Review of Artificial Immune System Based Security Frameworks for MANET <s> Clonal Selection <s> In electronics, there are two major classes of circuits, analog and digital electrical circuits. While digital circuits use discrete voltage levels, analog circuits use a continuous range of voltage. The synthesis of analog circuits is known to be a complex optimization task, due to the continuous behaviour of the output and the lack of automatic design tools; actually, the design process is almost entirely demanded to the engineers. In this research work, we introduce a new clonal selection algorithm, the elitist Immune Programming, ( eIP ) which uses a new class of hypermutation operators and a network-based coding. The eIP algorithm is designed for the synthesis of topology and sizing of analog electrical circuits; in particular, it has been used for the design of passive filters. To assess the effectiveness of the designed algorithm, the obtained results have been compared with the passive filter discovered by Koza and co-authors using the Genetic Programming ( GP ) algorithm. The circuits obtained by eIP algorithm are better than the one found by GP in terms of frequency response and number of components required to build it. <s> BIB002
According to the Clonal Selection Theory when the original lymphocyte is activated by binding to the antigen, clonal expansion of the original lymphocyte occurs. During the development of the lymphocyte, if any clone with antigen receptors corresponds to molecules of the organisms own body, it will be eliminated. In the clonal expansion of B-cells the average likeness increased for the antigen that sparked the clonal expansion through likeness maturation. Thus the B-cells are more effectively respond to antigens. Somatic hyper-mutation and the Selective mechanism lead to likeness maturation. Somatic hyper-mutation leads to a miscellany of antibodies by introducing random changes to the genes. Only those genes with a higher accord for the encountered antigen will survive. CLONALG was initially introduced in BIB001 and described in   . Ciccazzo et al. BIB002 proposed a variant of CLONALG termed Elitist Immune Programming (EIP). EIP is an extension of immune programming and the concept of elitism is borrowed from the immune inspired algorithm and is introduced to EIP. A new category of hyper-mutation operators and network based coding is used in EIP. Any hyper-mutation operator can only act on one node or link at a time. This work leads to the proposition of ten ad hoc network based hyper-mutation operators: add-parallel, add-series, delete component, mutate-component-value, copy-component-value, add-random-component, mutate-component-kind, link-modify, shrink and expand-node. The EIP algorithm was applied to a synthesis of topology and sizing of analog electrical circuits. Based on experiments the circuits designed by EIP were an improvement over that achieved using Genetic programming. Halavati et al.  included symbiosis to CLONALG and uses relatively specified antibodies which are an approximate solution only as they may not contain the data required. Each antibody will have just one property. Later the algorithm randomly selects an antibody to be included in an assembly. By using repetitive steps an assembly with all of the required properties is built, however, in cases where the algorithm fails to obtain an assembly, antibodies with random values are created for the missing portions and a new assembly is created. The technique of utilizing partially specified antibodies stems from the deduction that a problem can be broken into smaller problems and solutions to these smaller problems may provide an improved overall solution to the overarching problem. The approach in  proposed a variation of CLONALG for software mutation and testing that utilizes the notion of "memory individuals" that lead to the recognition of an antigen rather than using the notion of the CLONALG memory individuals per antigen. An antibody population is initialized with p tests either randomly generated or pre-specified. A periodic check is done by the algorithm searching for antibodies that will kill at least one mutant program. A Mutation Store is used to assess the freshness of an antibody and antibodies with a higher similarity score are added to the memory set in order to be returned to the tester. The productiveness of this method was compared against an elitist GA and the results showed that the proposed methodology produces a higher mutation score with lower computational cost. The Trend Evaluation Algorithm (TEA) proposed by Wilson  is similar to CLONALG however it incorporates a long term memory pool as well as short term memory pool by multiplying all of the bound trackers. The processes of Apoptosis and Mutation in the TEA occur across all population members. Consider the case where an antigen Ag containing 40 fictional price movements and ten Trends (T1-T10) is built to test the ability of the TEA to identify price trends. Antigen Ag is divided into four subsets Ag1, Ag2, Ag3, and Ag4. Ag1 contains two simple trends T1 and T2 and the more complex trends are involved in Ag2, Ag3 and Ag4. Experiments were done to test the algorithms capability to discern price trends as well as to probe the algorithms influence over the long term memory pool.