By Wen Jun Zhang
This necessary publication is the 1st of its variety on "selforganizology", the technological know-how of self-organization. It covers a variety of themes, reminiscent of the speculation, precept and method of selforganizology, agent-based modelling, intelligence foundation, ant colony optimization, fish/particle swarm optimization, mobile automata, spatial diffusion types, evolutionary algorithms, self-adaptation and keep watch over structures, self-organizing neural networks, disaster concept and techniques, and self-organization of organic groups, etc.Readers can have an in-depth and finished knowing of selforganizology, with special heritage details supplied should you desire to delve deeper into the topic and discover study literature.This publication is a useful reference for examine scientists, college lecturers, graduate scholars and high-level undergraduates within the components of computational technological know-how, synthetic intelligence, utilized arithmetic, engineering technological know-how, social technology and lifestyles sciences.
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Additional info for Selforganizology:The Science of Self-Organization
Under such a situation, information processes may be more fundamental than algorithms (Denining, 2010). Actually, Wolfram (2002) has argued that information processes underlie every natural process in the universe. ” As a consequence, Denining (2003, 2007, 2010), and Denning and Freeman (2009) developed a set of principles computing framework, which fall into seven categories: computation, communication, coordination, recollection, automation, evaluation, and design. page 32 October 29, 2015 11:37 Selforganizology: The Science of Self-Organization 9in x 6in Selforganizology: The Science of Self-organization b2246-ch02 33 Self-organization can be treated as an information process and a computation.
Diﬀerential equations, for example, can be rewrited into diﬀerence equations, and if it is possible, further resolved into many simple rules, until no further simpler rules can be found. Compared to complex rules, simple rules are more useful in exploiting the most fundamental mechanism of self-organization. In general, the simpler rules mean the more eﬀectiveness of simulation method used. In the sense of systematic simulation, selforganizology may be considered as the science based on self-organization, components, hierarchies, interactions, feedbacks, behaviors and rules, etc.
These properties make genetic algorithm to be widely used in combinatorial optimization, machine learning, signal processing, adaptive control, and artiﬁcial life. Genetic algorithm is considered the key technology that will signiﬁcantly impact the future of computing technology, along with adaptive systems, cellular automata, chaos theory, and artiﬁcial intelligence, etc. 2 Physics-based method The mechanism of these algorithms is diﬀerent from the genetic mechanism. They construct population-based optimization algorithm according to some physical laws.