A Strategy of Mutation History Learning in Immune Clonal Selection Algorithm.
ABSTRACT A novel strategy termed as mutation history learning strategy (MHLS) is proposed in this paper. In MHLS, a vector called mutation
memory is introduced for each antibody and a new type of mutation operation based on mutation memory is also designed. The
vector of mutation memory is learned from a certain antibody’s iteration history and used as guidance for its further evolution.
The learning and usage of history information, which is absent from immune clonal selection algorithm (CSA), is shown to be
an efficient measure to guide the direction of the evolution and accelerate algorithm’s converging speed. Experimental results
show that MHLS improves the performance of CSA greatly in dealing with the function optimization problems.
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Conference Proceeding: Procedure cloning.[show abstract] [hide abstract]
ABSTRACT: Procedure cloning is an interprocedural optimization where the compiler creates specialized copies of procedure bodies. The authors examine the problem of procedure cloning and describe an experiment where cloning was required to make other transformations possible. They present a three-phase algorithm for deciding how to clone a program and analyze the algorithm's complexity. The algorithm finds potential improvements in forward interprocedural data-flow solutions and clones those procedures that lead to sharper information. A set of assumptions that bound both the running time of the algorithm and the expansion in code size is presentedICCL'92, Proceedings of the 1992 International Conference on Computer Languages, Oakland, California, USA, 20-23 Apr 1992; 01/1992
Conference Proceeding: Advanced clone-analysis to support object-oriented systemrefactoring[show abstract] [hide abstract]
ABSTRACT: Manual source code copy and modification is often used by programmers as an easy means for functionality reuse. Nevertheless, such practice produces duplicated pieces of code or clones whose consistent maintenance might be difficult to achieve. It also creates implicit links between classes sharing a functionality. Clones are therefore good candidates for system redesign. This paper presents a novel approach for computer-aided clone-based object-oriented system refactoring. The approach is based on an advanced clone analysis which focuses on the extraction of clone differences and their interpretation in terms of programming language entities. It also focuses on the study of contextual dependencies of cloned methods. The clone analysis has been applied to JDK 1.1.5, a large scale system of 150 KLOCReverse Engineering, 2000. Proceedings. Seventh Working Conference on; 02/2000
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ABSTRACT: The clonal selection algorithm is used by the natural immune system to define the basic features of an immune response to an antigenic stimulus. It establishes the idea that only those cells that recognize the antigens are selected to proliferate. The selected cells are subject to an affinity maturation process, which improves their affinity to the selective antigens. In this paper, we propose a powerful computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response. The algorithm is shown to be an evolutionary strategy capable of solving complex machine- learning tasks, like pattern recognition and multi- modal optimization.01/2000;