Based on vaccine extraction and immune optimum rough sets attribute reduction.
ABSTRACT Based on the inspiration of immune system, a prior knowledge of attribute kernel as bacterins is introduced to antibody coding and the population is vaccinated in a stochastic way. The classification approximation quality is took as the antibody affinity, conduct clonal selection to enhance the diversity of antibody and affinity maturation. Through those modifications, solves the problem of quickly obtain minimum reductions and more reductions of the rough sets. Experimental results illustrate that the approach is an effective and quick way in solving attribute reduction. The simulation results illustrated it's efficiency and verified it's remarkable quality of the global and local convergence reliability.
- SourceAvailable from: Marzena Kryszkiewicz[show abstract] [hide abstract]
ABSTRACT: In the paper we present Rough Set approach to reasoning in incomplete information systems. We propose reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making. In our approach we make only one assumption about unknown values: the real value of a missing attribute is one from the attribute domain. However, we do not assume which one. We show how to find decision rules directly from such an incomplete decision table, which are as little non-deterministic as possible and have minimal number of conditions.Information Sciences. 01/1998;
Conference Proceeding: Multimodal Search with Immune Based Genetic Programming.[show abstract] [hide abstract]
ABSTRACT: Artificial Immune System has been regarded an effective powerful optimization framework because of its powerful information processing capabilities. Natural immune system has many features such as memorizing ability, singularity against antigens, flexibility against dynamically changing environments, and diversity of antibody. Up to now, several algorithms inspired by these immune features have been proposed and applied to many problems. However, Genetic Programming with immune features which is capable of solving multimodal problems has not been proposed. This paper proposes an optimization algorithm named Multimodal Search Genetic Programming (MSGP), which extends GP by introducing the immunological feature so as to solve the problems with multimodal fitness landscape. We empirically show the effectiveness of our approach by applying the algorithm to the gene classification problem and the HP protein folding problem.Artificial Immune Systems, Third International Conference, ICARIS 2004, Catania, Sicily, Italy, September 13-16, 2004; 01/2004
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ABSTRACT: 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 optimizationIEEE Transactions on Evolutionary Computation 07/2002; · 4.81 Impact Factor