Theory and practice of natural computing: seventh edition

Carlos Martín-Vide, Miguel A. Vega-Rodríguez
2021 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
Advice, Brussels/London. TPNC 2018 was the seventh event in a series dedicated to presenting and promoting research on the wide spectrum of computational principles, models, and techniques inspired by information processing in nature. We intended to attract both theoretical and applied contributions to nature-inspired models of computation, synthesizing nature by means of computation, nature-inspired materials, and information processing in nature. Out of 69 submissions to the conference, 35
more » ... ers were accepted (which represents an acceptance rate of about 51%). Among them, the authors of 14 papers were invited to submit to this special issue. Each submission was reviewed by three experts and, based on their comments, the guest editors decided to accept 4 papers (which represents an acceptance rate of about 6% out of the submissions to the conference). Next, we briefly present the papers included in this special issue. In the paper Characterization and Computation of Ancestors in Reaction Systems, Roberto Barbuti, Anna Bernasconi, Roberta Gori, and Paolo Milazzo tackle the computational complexity of preimages and nth ancestors in reaction systems. Despite the intractability of many of the problems, the authors find a Boolean formula computable in polynomial time that, once duly simplified, allows solving ancestor problems for classes of reaction systems in polynomial time. Gaussian-kernel c-means Clustering Algorithms, by Shou-Jen Chang-Chien, Yessica Nataliani, and Miin-Shen Yang, deal with algorithms for clustering for datasets in a noisy environment or with diverse shape clusters. The authors use Gaussian kernels to generalize known AHCM and AFCM algorithms. Experimental results and comparisons show that the new algorithms improve the performance of previous ones.
doi:10.1007/s00500-020-05526-y fatcat:kuonjlf5fjc3pghkql7dqefwle