This book highlights recent research on interval methods for solving nonlinear constraint satisfaction, optimization and similar problems. Further, it presents a comprehensive survey of applications in various branches of robotics, artificial intelligence systems, economics, control theory, dynamical systems theory, and others. Three appendices, on the notation, representation of numbers used as intervals’ endpoints, and sample implementations of the interval data type in several programming languages, round out the coverage.

This book is about the design and development of tools for software testing. It intends to get the reader involved in software testing rather than simply memorizing the concepts. The source codes are downloadable from the book website. The book has three parts: software testability, fault localization, and test data generation. Part I describes unit and acceptance tests and proposes a new method called testability-driven development (TsDD) in support of TDD and BDD. TsDD uses a machine learning model to measure testability before and after refactoring. The reader will learn how to develop the testability prediction model and write software tools for automatic refactoring. Part II focuses on developing tools for automatic fault localization. This part shows the reader how to use a compiler generator to instrument source code, create control flow graphs, identify prime paths, and slice the source code. On top of these tools, a software tool, Diagnoser, is offered to facilitate experimenting with and developing new fault localization algorithms. Diagnoser takes a source code and its test suite as input and reports the coverage provided by the test cases and the suspiciousness score for each statement. Part III proposes using software testing as a prominent part of the cyber-physical system software to uncover and model unknown physical behaviors and the underlying physical rules. The reader will get insights into developing software tools to generate white box test data.

The two-volume set LNCS 12043 and 12044 constitutes revised selected papers from the 13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019, held in Bialystok, Poland, in September 2019. The 91 regular papers presented in these volumes were selected from 161 submissions. For regular tracks of the conference, 41 papers were selected from 89 submissions. The papers were organized in topical sections named as follows: Part I: numerical algorithms and parallel scientific computing; emerging HPC architectures; performance analysis and scheduling in HPC systems; environments and frameworks for parallel/distributed/cloud computing; applications of parallel computing; parallel non-numerical algorithms; soft computing with applications; special session on GPU computing; special session on parallel matrix factorizations. Part II: workshop on language-based parallel programming models (WLPP 2019); workshop on models algorithms and methodologies for hybrid parallelism in new HPC systems; workshop on power and energy aspects of computations (PEAC 2019); special session on tools for energy efficient computing; workshop on scheduling for parallel computing (SPC 2019); workshop on applied high performance numerical algorithms for PDEs; minisymposium on HPC applications in physical sciences; minisymposium on high performance computing interval methods; workshop on complex collective systems. Chapters "Parallel adaptive cross approximation for the multi-trace formulation of scattering problems" and "A High-Order Discontinuous Galerkin Solver with Dynamic Adaptive Mesh Refinement to Simulate Cloud Formation Processes" of LNCS 12043 are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model. Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

In the first approximation, decision making is nothing else but an optimization problem: We want to select the best alternative. This description, however, is not fully accurate: it implicitly assumes that we know the exact consequences of each decision, and that, once we have selected a decision, no constraints prevent us from implementing it. In reality, we usually know the consequences with some uncertainty, and there are also numerous constraints that needs to be taken into account. The presence of uncertainty and constraints makes decision making challenging. To resolve these challenges, we need to go beyond simple optimization, we also need to get a good understanding of how the corresponding systems and objects operate, a good understanding of why we observe what we observe – this will help us better predict what will be the consequences of different decisions. All these problems – in relation to different application areas – are the main focus of this book.

This book is an overview of latest successes and applications of fuzzy techniques—techniques that use expert knowledge formulated by natural-language words like "small". Engineering applications deal with aerospace (control of spacecrafts and unmanned aerial vehicles, air traffic control, airport passenger flow predictions), materials (designing gold nano-structures for medicine, catalysis, and sensors), and robot navigation and manipulation. Other application areas include cosmology, demographics, finances, wine production, medicine (diagnostics, epidemics control), and predicting human behavior. In many cases, fuzzy techniques are combined with machine learning AI. Due to natural-language origin of fuzzy techniques, such combination adds explainability (X) to AI. This book is recommended to students and practitioners interested in the state-of-the-art fuzzy-related XAI and to researchers willing to take on numerous remaining challenges.

Theformulationofmanypracticalproblemsnaturallyinvolvesconstraintsonthe variables entering the mathematical model of a real-life situation to be analyzed. It is of great interest to ?nd the possible scenarios satisfying all constraints, and, iftherearemanyofthem,eitherto?ndthebestsolution,ortoobtainacompact, explicit representation of the whole feasible set. The 2nd Workshop on Global Constrained Optimization and Constraint S- isfaction, COCOS 2003, which took place during November 18–21, 2003 in L- sanne, Switzerland, was dedicated to theoretical, algorithmic, and application oriented advances in answering these questions. Here global optimization refers to ?nding the absolutely best feasible point, while constraint satisfaction refers to?ndingallpossiblefeasiblepoints.AsinCOCOS2002,the?rstsuchworkshop (see the proceeedings [1]), the emphasis was on complete solving techniques for problems involving continuous variables that provide all solutions with full rigor, and on applications which, however, were allowed to have relaxed standards of rigor. The participants used the opportunity to meet experts from global optimi- tion, mathematical programming, constraint programming, and applications, and to present and discuss ongoing work and new directions in the ?eld. Four invited lectures and 20 contributed talks were presented at the workshop. The invited lectures were given by John Hooker (Logic-Based Methods for Global Optimization), Jean-Pierre Merlet (Usual and Unusual Applications of Interval Analysis), Hermann Schichl (The COCONUT Optimization Environment), and Jorge Mor ́ e (Global Optimization Computational Servers). This volume contains the text of Hooker’s invited lecture and of 12 c- tributed talks. Copies of the slides for most presentations can be found at [2]. Constraintsatisfactionproblems.Threepapersfocusonalgorithmicaspects of constraint satisfaction problems.

This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Global Constraints Optimization and Costraint Satisfaction, COCOS 2002, held in Valbonne-Sophia Antipolis, France in October 2002. The 15 revised full papers presented together with 2 invited papers were carefully selected during two rounds of reviewing and improvement. The papers address current issues in global optimization, mathematical programming, and constraint programming; they are grouped in topical sections on optimization, constraint satisfaction, and benchmarking.

This book constitutes the refereed proceedings of the 18th International Conference on Computational Methods in Systems Biology, CMSB 2020, held in Konstanz, Germany, in September 2020.* The 17 full papers and 5 tool papers were carefully reviewed and selected from 30 submissions. In addition 3 abstracts of invited talks and 2 tutorials have been included in this volume. Topics of interest include formalisms for modeling biological processes; models and their biological applications; frameworks for model verification, validation, analysis, and simulation of biological systems; high-performance computational systems biology and parallel implementations; model inference from experimental data; model integration from biological databases; multi-scale modeling and analysis methods; computational approaches for synthetic biology; and case studies in systems and synthetic biology. * The conference was held virtually due to the COVID-19 pandemic.