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Innovative computational intelligence : a rough guide to 134 clever algorithms
Xing B., Gao W., Springer Publishing Company, Incorporated, Cham, Switzerland, 2014. 260 pp. Type: Book (978-3-319034-03-4)
Date Reviewed: Sep 25 2014

Computational intelligence is distinguished from classic algorithmic development because it is based on or inspired by real-world mechanisms as opposed to a mostly pure mathematical or logical analysis. Bezdek [1], based on his earlier work, wrote the first paper dedicated to computational intelligence and is believed to have coined the phrase. Often, natural world processes outperform the synthetic technologies constructed by humans to simulate them. Human vision is learned without any special effort, but robotic vision is still under development. Humans are able to balance themselves despite the multitude of muscles, bones, and tissues that comprise the body. Scientists have had limited success in the degrees of freedom (joints) that robotic limbs can control.

This book tries to summarize 134 “clever” algorithms that are inspired by scientific processes found in nature. The authors provide a listing of these at the beginning of the book, along with the accepted acronyms. The first chapter (Part 1) divides these algorithms into four categories (Parts 2 to 5): those based on biology, physics, chemistry, and mathematics, in that order. Each subsection briefly introduces the category and lists all of the algorithms that fit into that category, with references to the sources originally discussing the algorithms. It is interesting to see a category that is primarily mathematically based, although it only contains two algorithms: the base optimization algorithm, which is comparable to an evolutionary algorithm, and matheuristics, which is a hybridization of metaheuristics (experimental in nature and naturally inspired) and mathematical programming. Chapters 27 and 28, respectively, elaborate on these algorithms.

The majority of the book (chapters 2 through 17) report on biologically inspired algorithms (Part 2) for computational intelligence. The variety of models for these algorithms includes the behaviors of the following organisms: bacteria, bats, bees, cats, cuckoos, fish, frogs, fruit flies, invasive weeds, and luminous insects (each presented in a separate chapter). The remaining chapters in this part are broader, for example, chapter 5 is based on biogeography, chapter 12 on group search optimization, chapter 15 on imperialist competition, and chapter 16 on teacher-learner interactions. An interesting chapter that was included in this part of the book is chapter 14 on musically inspired algorithms. These include algorithms based on musical concepts such as harmony, melody, and composition. One might have expected to find this chapter in the section utilizing physics (Part 3) or in Part 5 on integrating mathematics. Chapter 17, on emerging biological concepts, presents 56 algorithms and is the longest chapter (100 pages) in the book.

Part 3, on physics, contains seven chapters. The physics concepts incorporated include the big bang theory, centripetal forces, charged systems (Coulomb’s law), electromagnetism, gravitational forces, and the falling (dropping) of water. Chapter 24 (40 pages) presents 22 algorithms based on emerging concepts in physics.

The remaining parts of the book (4 and 5) have two chapters each. The chemistry section (Part 4) has a general chapter on chemical reactions and one on emergent concepts. Part 5 on mathematics has the two algorithms mentioned above.

This book is unique both for its breadth of algorithms as well as its focused attention on computational intelligence. It is a necessary text for anyone interested in exploring the varieties of naturally inspired algorithms.

Reviewer:  Minette Carl Review #: CR142762 (1501-0028)
1) Bezdek, J. What is computational intelligence?. In Computational intelligence: imitating life. Edited by Marks, R. J., II; Zurada, J. M.; Robinson, C. J. IEEE Press, 1994, 1–11.
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