Numerical and Evolutionary Optimization 2020

This book was established after the 8th International Workshop on Numerical and Evolutionary Optimization (NEO), representing a collection of papers on the intersection of the two research areas covered at this workshop: numerical optimization and evolutionary search techniques. While focusing on the design of fast and reliable methods lying across these two paradigms, the resulting techniques are strongly applicable to a broad class of real-world problems, such as pattern recognition, routing, energy, lines of production, prediction, and modeling, among others. This volume is intended to serve as a useful reference for mathematicians, engineers, and computer scientists to explore current issues and solutions emerging from these mathematical and computational methods and their applications

The Fuzziness in Molecular, Supramolecular, and Systems Chemistry

Fuzzy Logic is a good model for the human ability to compute words. It is based on the theory of fuzzy set. A fuzzy set is different from a classical set because it breaks the Law of the Excluded Middle. In fact, an item may belong to a fuzzy set and its complement at the same time and with the same or different degree of membership. The degree of membership of an item in a fuzzy set can be any real number included between 0 and 1. This property enables us to deal with all those statements of which truths are a matter of degree. Fuzzy logic plays a relevant role in the field of Artificial Intelligence because it enables decision-making in complex situations, where there are many intertwined variables involved. Traditionally, fuzzy logic is implemented through software on a computer or, even better, through analog electronic circuits. Recently, the idea of using molecules and chemical reactions to process fuzzy logic has been promoted. In fact, the molecular word is fuzzy in its essence. The overlapping of quantum states, on the one hand, and the conformational heterogeneity of large molecules, on the other, enable context-specific functions to emerge in response to changing environmental conditions. Moreover, analog input–output relationships, involving not only electrical but also other physical and chemical variables can be exploited to build fuzzy logic systems. The development of “fuzzy chemical systems” is tracing a new path in the field of artificial intelligence. This new path shows that artificially intelligent systems can be implemented not only through software and electronic circuits but also through solutions of properly chosen chemical compounds. The design of chemical artificial intelligent systems and chemical robots promises to have a significant impact on science, medicine, economy, security, and wellbeing. Therefore, it is my great pleasure to announce a Special Issue of Molecules entitled “The Fuzziness in Molecular, Supramolecular, and Systems Chemistry.” All researchers who experience the Fuzziness of the molecular world or use Fuzzy logic to understand Chemical Complex Systems will be interested in this book

Homology Modeling of Myoglobin using Adaptive Neurofuzzy Systems

The problem of nonlinear system identification as applied to protein folding problem is discussed in this paper. The mapping of amino acid sequence and the atomic coordinates of the alpha carbon atoms of the amino acids in a protein is typically a nonlinear problem. System identification is performed using adaptive neurofuzzy techniques. Various ANFIS models are created and the model with the least error is selected. The protein ‘Human Myoglobin Mutant (PDB Id: 2MM1) ’ and its homologue ‘Pig Metmyoglobin (PDB Id: 1MYH) ’ have been used for the creation and training of the ANFIS model. The tertiary structure of 3 proteins ‘Myoglobin (Horse Heart) wild type complexed with nitrosoethane (PDB Id: 1NPG)’, ‘Loggerhead sea turtle Myoglobin (PDB Id: 1LHS) ’ and ‘MetMyoglobin from Yellowfin Tuna (PDB Id: 1MYT) ’ have been predicted using the same ANFIS model. It is found that the root mean square errors in the prediction of the tertiary structures of the 3 proteins considered in this study are 4.32, 3.04 and 3.00 respectively. 1