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Linear Models for Optimal Test Design
Begins with a reflection on the history of test design--the core activity of all educational and psychological testing. It then presents a standard language for modeling test design problems as instances of multi-objective constrained optimization. The main portion of the book discusses test design models for a large variety of problems from the daily practice of testing, and illustrates their use with the help of numerous empirical examples. The presentation includes models for the assembly of tests to an absolute or relative target for their information functions, classical test assembly, test equating problems, item matching, test splitting, simultaneous assembly of multiple tests, tests with item sets, multidimensional tests, and adaptive test assembly. Two separate chapters are devoted to the questions of how to design item banks for optimal support of programs with fixed and adaptive tests. Linear Models for Optimal Test Design, which does not require any specific mathematical background, has been written to be a helpful resource on the desk of any test specialist.
Knowledge-Driven Computing : Knowledge Engineering and Intelligent Computations
Knowledge-Driven Computing constitutes an emerging area of intensive research located at the intersection of Computational Intelligence and Knowledge Engineering with strong mathematical foundations. It embraces methods and approaches coming from diverse computational paradigms, such as evolutionary computation and nature-inspired algorithms, logic programming and constraint programming, rule-based systems, fuzzy sets and many others. The use of various knowledge representation formalisms and knowledge processing and computing paradigms is oriented towards the efficient resolution of computationally complex and difficult problems.
Knowledge Discovery in Inductive Databases ; Vol.3933 ; 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers
The 4th International Workshop on Knowledge Discovery in Inductive Databases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed.
Knowledge Discovery in Inductive Databases ; 5th International Workshop, KDID 2006 Berlin, Germany, September 18th, 2006 Revised Selected and Invited Papers
Constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006. The papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.
Cellular Genetic Algorithms
CELLULAR GENETIC ALGORITHMS defines a new class of optimization algorithms based on the concepts of structured populations and Genetic Algorithms (GAs). The authors explain and demonstrate the validity of these cellular genetic algorithms throughout the book. This class of genetic algorithms is shown to produce impressive results on a whole range of domains, including complex problems that are epistatic, multi-modal, deceptive, discrete, continuous, multi-objective, and random in nature. The focus of this book is twofold. On the one hand, the authors present new algorithmic models and extensions to the basic class of Cellular GAs in order to tackle complex problems more efficiently. On the other hand, practical real world tasks are successfully faced by applying Cellular GA methodologies to produce workable solutions of real-world applications.
Bioinspired optimization methods and their applications ; 9th International conference, BIOMA 2020, Brussels, Belgium, November 19–20, 2020, Proceedings
This book constitutes the refereed proceedings of the 9th International Conference on Bioinspired Optimization Methods and Their Applications, BIOMA 2020, held in Brussels, Belgium, in November 2020. The 24 full papers presented in this book were carefully reviewed and selected from 68 submissions. The papers in this BIOMA proceedings specialized in bioinspired algorithms as a means for solving the optimization problems and came in two categories: theoretical studies and methodology advancements on the one hand, and algorithm adjustments and their applications on the other.
Artificial neural network-based optimized design of reinforced concrete structures
Introduces AI-based Lagrange optimization techniques that can enable more rational engineering decisions for concrete structures while conforming to codes of practice. It shows how objective functions including cost, CO2 emissions, and structural weight of concrete structures are optimized either separately or simultaneously while satisfying constraining design conditions using an ANN-based Lagrange algorithm. Any design target can be adopted as an objective function. Many optimized design examples are verified by both conventional structural calculations and big datasets. Uniquely applies the new powerful tools of AI to concrete structural design and optimization Multi-objective functions of concrete structures optimized either separately or simultaneously Design requirements imposed by codes are automatically satisfied by constraining conditions Heavily illustrated in color with practical design examples
Artificial intelligence in drug design
Looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future.
Artificial immune systems ; Vol. 4163 : 5th International conference, ICARIS 2006, Oeiras, Portugal, September 4-6, 2006, Proceedings
ICARIS 2006 is the ?fth instance of a series of conferences dedicated to the comprehension and the exploitation of immunological principles through their translation into computational terms.Their axis of research tries to stabilize an on-going identity somewhere in the crossroad of engineering (building useful artifacts), natural sciences (biologyor psychology— improving the comprehension and prediction of natural phenomena) and t- oretical computer sciences (developing and mastering the algorithmic world). Accordingly and depending on which of these perspectives receives more s- port, they attempt at attracting di?erent kinds of scientists and at stimul- ing di?erent kinds of scienti?c attitudes. For many years and in the previous ICARIS conferences, it was clearly the “engineering.
Advances in Swarm Intelligence ; 11th International Conference, ICSI 2020, Belgrade, Serbia, July 14–20, 2020, Proceedings
Constitutes the proceedings of the 11th International Conference on Advances in Swarm Intelligence, ICSI 2020, held in July 2020 in Belgrade, Serbia. Due to the COVID-19 pandemic the conference was held virtually. The 63 papers included in this volume were carefully reviewed and selected from 127 submissions. The papers are organized in 12 cohesive topical sections as follows: Swarm intelligence and nature-inspired computing; swarm-based computing algorithms for optimization; particle swarm optimization; ant colony optimization; brain storm optimization algorithm; bacterial foraging optimization; genetic algorithm and evolutionary computation; multi-objective optimization; machine learning; data mining; multi-agent system and robotic swarm, and other applications.
Advances in Metaheuristics for Hard Optimization
The book gathers contributions related to the following topics: theoretical developments in metaheuristics; adaptation of discrete metaheuristics to continuous optimization; performance comparisons of metaheuristics; cooperative methods combining different approaches; parallel and distributed metaheuristics for multiobjective optimization; software implementations; and real-world applications.
Advances in Differential Evolution
Differential evolution is arguably one of the hottest topics in today's computational intelligence research. This book seeks to present a comprehensive study of the state of the art in this technology and also directions for future research.
