物件導向軟體之架構(Object-Oriented Software Construction)探討

Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.

/pdf/evolving-artificial-neural-networks-4kzrbu7pkn.pdf

Evolving artificial neural networks

https://www.ufz.de/export/data/2/100065_GrimmODD.pdf

A standard protocol for describing individual-based and agent-based models

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity.

/pdf/pattern-oriented-modeling-of-agent-based-complex-systems-43ebw1z7y9.pdf

Pattern-oriented modeling of agent-based complex systems: lessons from ecology

From the Publisher:
Although twenty-five years have passed since the first edition of this classical text, the world has seen many advances in modeling and simulation, the need for a widely accepted framework and theoretical foundation is even more necessary today. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation has been made easier and faster by riding piggyback on advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now front and center under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies. 
This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition. 
This edition presents a rigorous mathematical foundation for modeling and simulation. Also, it now provides a comprehensive framework for integrating the various simulation approaches employed in practice. Including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets. A unifying concept, called the DEVS Bus, enables models, as expressed in their native formalisms, to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. If you are doing integrative simulations, whether or not they are HLA compliant, this is the only book available to provide the foundation to understand, simplify and successfully accomplish your task. 
Herbert Praehofer is an Assistant Professor at the Johannes Kepler University in Linz, Austria. He has over 50 publications in international journals and conference proceedings on Modeling and Computer Simulation, Systems Theory, and Software Engineering. 
Tag Gon Kim is a Professor of Electrical Engineering at the Korea Advanced Institutes of Science and Technology (KAIST), Taejon, Korea. His research interests include discrete event systems modeling/simulation, computer/communication systems analysis, and object-oriented simulation engineering. He is a senior member of IEEE and SCS, and a member of ACM. 
* Provides a comprehensive framework for continuous and discrete event modeling and simulation
* Explores the mathematical foundation of simulation modeling 
* Discusses system morphisms for model abstraction and simplification
* Presents a new approach to discrete event simulation of continuous processes 
* Includes parallel and distributed simulation of discrete event models 
* Presentation of a concept to achieve simulator interoperability in the form of the DEVS-Bus
* Complete coverage necessary for compliance with High Level Architecture (HLA) standards

Bernard P Zeigler, is a Professor of Electrical & Computer Engineering at the University of Arizona and heads the Artificial Intelligence Simulation Research Group. He is the author of numerous books and publications, and he is the Editor-in-Chief of the Transactions of the Society for Computer Simulation International.

Theory of modeling and simulation

Part I: Basics. Introduction to Systems Modeling Concepts. Framework for Modeling and Simulation. Modeling Formalisms and Their Simulators. Introduction to Discrete Event System Specifications (DEVS). Hierarchy of System Specifications. Part II: Modeling Formalisms and Simulation Algorithms. Basic Formalisms: DEVS, DTSS, DESS. Basic Formalisms: Coupled Multicomponent Systems. Simulators for Basic Formalisms. Multiformalism Modeling and Simulation. DEVS-Based Extended Formalisms. Parallel and Distributed Discrete Event Simulation. Part III: System Morphisms: Abstraction, Representation, Approximation. Hierarchy of System Morphisms. Abstraction: Constructing Model Families. Verification, Validation, Approximate Morphisms: Living with Error. DEVS and DEVS-like Systems: Universality and Uniqueness. DEVS Representation of Systems. Part IV: System Design and Modeling and Simulation Environments. DEVS-Based Design Methodology. System Entity Structure/Model Base Framework. Collaboration and the Future.

Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems

We present a time domain extension of the hierarchical and modular discrete event specification (DEVS) formalism. This extension is important for establishing a seamless real-time software development framework. Formalisms help describe a system unambiguously. If formal models are implemented without any consistent frameworks, however, it is hard to guarantee that there is no semantic gap between models and codes. Real-Time DEVS, named RTDEVS, is an extension of DEVS that can be characterized in three perspectives: the real time execution of models, the addition of time interval functions, and the activity specification for each state. After analyzing a system, the framework based on RTDEVS helps to expand each model of the system for executing in a real-time environment. In order to support the RTDEVS formalism, we propose abstract executive concepts based on the abstract simulator concepts of the DEVS formalism. Also, we implement an RTDEVS execution engine, named DEVS Executive, which runs on real-time Mach.

A Real-Time Discrete Event System Specification Formalismfor Seamless Real-Time Software Development

Recently, big data has received greater attention in diverse research fields, including medicine, science, engineering, management, defense, politics, and others. Such research uses big data to pre...

https://journals.sagepub.com/doi/pdf/10.1177/0037549717692866

Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system:

System entity structure (SES) is a structural knowledge representation scheme that contains knowledge of decomposition, taxonomy, and coupling of a system. Formally, the SES is a labeled tree with attached variable types that satisfy certain axioms. Described is a realization of the SES in Scheme (a Lisp dialect), that is called ESP-Scheme. Specifically, the computer representation of SES and main operations on SES are presented, and then facilities provided by ESP-Scheme are described. Two examples of application are discussed: a parallel processor model and a simulation study of a university phone registration system. ESP-Scheme acts as a model base management system in DEVS-Scheme, a knowledge-based simulation environment. It supports specification of the structure of a family of models, pruning the structure to a reduced version, and transforming the latter to a simulation model by synthesizing component models in the model base. >

Tag Gon Kim

Papers

Theory of modeling and simulation

Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems

A Real-Time Discrete Event System Specification Formalismfor Seamless Real-Time Software Development

Data modeling versus simulation modeling in the big data era: case study of a greenhouse control system:

System entity structuring and model base management