Selforganizology

The following sections are included:

• Dedication
• Preface
• Contents

To better understand self-organization, it is necessary to have a basic knowledge on organization and organizational theory. Some details can be found in Zhao and Zhang (2013).

The scientific terminology, selforganizology, originated from Zhang (2013a). The journal, Selforganizology, can be referenced at http:// www.iaees.org/publications/journals/selforganizology/selforganizology.asp, for some of the most recent advances. In this chapter, I will discuss selforganizology with more details.

Agent-based modeling is a relatively new methodology and has been used in various fields. More details on agent-based modeling can be found in Zhang (2012).

Some of the research methods on artificial intelligence can be divided into two categories. The first category includes methods for symbol processing. They are based on the hypothesis on physical symbol system of Newell and Simon. Almost most of them are called “classic artificial intelligence.” These methods use statements to express the knowledge of problem domain. These statements are based on, or substantially equivalent to the first-order logic statements. They use logical inference to derive results of the knowledge. These methods require sufficient knowledge of the problem domain, and thus are usually known as the knowledge-based approach. The second category comprises so-called “sub-symbols” methods. They usually adopt a bottom-up approach, i.e., address a problem from the bottom to the top. They also learn from the environment and stress the symbolic foundation. In the physical hypothesis, an agent does not use a centralized model, but uses different behavior modules to interact with the environment and conducts complex behaviors. An agent/component in the self-organizing systems usually possesses certain intelligence. The present chapter discusses some methods for artificial intelligence research.

Catastrophe theory is a science based on topology and stability theory (bifurcation theory and singularity theory) (Thom, 1969, 1975, 1977; Zeeman, 1976, 1977; Zhao and Yu, 1987). It studies the behavior of system at equilibrium points, uncovers the properties of sudden changes in state variables during the process of continuous and smooth change of control variables. In catastrophe theory, we study the uncontinuous properties of potential function around its critical points and develop the qualitative models…

The following sections are included:

• Self-adaptation
• Control Systems

The following sections are included:

• Cellular Automata
• Cellular Automata for Population Diffusion
• Spatial Diffusion Models
• Epidemic System

Artificial neural networks learn from the environment in an unsupervised (for example, selforganizing neural networks) or supervised way (training samples; for example, feedback neural networks). Principles, rules and algorithms of artificial neural networks, in particular the self-organizing and feedback neural networks, are of importance to self-organization research. This chapter describes self-organizing and feedback neural networks, learning rules, architecture determination, and mechanism interpretability of neural networks. More details can be found in Zhang (2010), Yang and Zhang (2000), and many others.

The following sections are included:

• Proposition of Ant Colony Optimization
• Principles and Characteristics of ACO
• Algorithms of ACO
• Applications of ACO

The following sections are included:

• Artificial Fish Swarm Algorithm (AFSA)
• Particle Swarm Optimization (PSO)

The following sections are included:

• Synergy and Biological Evolution
• Evolutionary Programming (EP)
• Evolutionary Strategies (ES)
• Differential Evolution Algorithm (DEA)
• Quantum-inspired Evolutionary Algorithm (QEA)
• Genetic Algorithms (GAs)

Correlation analysis helps to exploit the synergic mechanism. This chapter describes some general correlation measures and their partial correlations.

There are no control and organizing centres in the evolution of community succession and assembly (Whittaker, 1979). It is a selforganizing process (Zhang, 2014b). A biological community is a self-organizing system, generated from repeating species invasions, selection, adaptation, and optimization. According to Young et al. (2001), community succession refers to the regular and predictable process of replacement of species composition in the environment such that all species have been eliminated or disturbed. Succession likely leads to the restoration of the community to the previous nondisturbed state. Community assembly is the process that species grow and interact to establish a community. Community assembly stresses the change of community over a single phase (Warren et al., 2003). Community assembly may likely lead to a diverse and stable community…

Selforganizology covers a large number of mathematical areas, e.g., probability theory, numerical computation, differential equations, etc. Mathematical foundations are thus discussed in this chapter. Some details can be found in Zhang (2010, 2012a).

The following sections are included:

• References
• Index