An immunogenetic approach in chemical spectrum recognition

This chapter describes an immunogenetic approach to recognize spectra for chemical analysis. In particular, an immunological model for chemical reactions is described in which a population of specialists for each of the possible products is evolved using a genetic algorithm. Accordingly, a small well-trained specialist library is established for testing their pattern recognition ability. The model was experimented with several real-world datasets to identify components in chemical spectra (such as IR spectra and Raman spectra). Experimental results exhibit the performance of the approach in finding correct products that correspond to an input spectrum, specifically, for a composite spectrum in which there are multiple products physically mixed. It would be very difficult to interpret otherwise.

[1]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[2]  Yuehua Cao,et al.  Constructing Surface Roughness of Silver for Surface-Enhanced Raman Scattering by Self-Assembled Monolayers and Selective Etching Process , 1999 .

[3]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[4]  Dipankar Dasgupta,et al.  Novelty detection in time series data using ideas from immunology , 1996 .

[5]  Alan S. Perelson,et al.  The Baldwin effect in the immune system: learning by somatic hypermutation , 1996 .

[6]  Hugues Bersini,et al.  The Immune Recruitment Mechanism: A Selective Evolutionary Strategy , 1991, ICGA.

[7]  T. Fukuda,et al.  Immune Networks Using Genetic Algorithm for Adaptive Production Scheduling , 1993 .

[8]  Charles L. Wilkins,et al.  Neural network assisted rapid screening of large infrared spectral databases , 1995 .

[9]  Alan S. Perelson,et al.  Searching for Diverse, Cooperative Populations with Genetic Algorithms , 1993, Evolutionary Computation.

[10]  Peter Ross,et al.  Producing robust schedules via an artificial immune system , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[11]  J. Davenport Editor , 1960 .

[12]  Zbigniew Michalewicz,et al.  Evolutionary Computation 1 , 2018 .

[13]  Kenneth A. De Jong,et al.  The Coevolution of Antibodies for Concept Learning , 1998, PPSN.

[14]  Jongsoo Lee,et al.  GA BASED SIMULATION OF IMMUNE NETWORKS APPLICATIONS IN STRUCTURAL OPTIMIZATION , 1997 .

[15]  Alan S. Perelson,et al.  Genetic Algorithms and the Immune System , 1990, PPSN.

[16]  Alan S. Perelson,et al.  Using Genetic Algorithms to Explore Pattern Recognition in the Immune System , 1993, Evolutionary Computation.

[18]  Searching a mid-infrared spectral library of solids and liquids with spectra of mixtures , 1997 .

[19]  Alan S. Perelson,et al.  The Evolution of Emergent Organization in Immune System Gene Libraries , 1995, ICGA.

[20]  Jongsoo Lee,et al.  Constrained genetic search via schema adaptation: An immune network solution , 1996 .

[21]  D. Dasgupta,et al.  Immunity-based systems: a survey , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[22]  Akira Fujishima,et al.  Examination of the Photoreaction of p-Nitrobenzoic Acid on Electrochemically Roughened Silver Using Surface-Enhanced Raman Imaging (SERI) , 1998 .

[23]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .