This book is a collection of ten independent research papers by different authors that mostly report experimental results about heuristics for solving optimization problems. Though familiar with academic texts, I cannot access their academic contributions since they are not from my area of expertise.
The heuristics considered are inspired by ideas taken from nature: Genetic algorithms are most common, but cellular automata, simulated annealing and neural networks are also considered. However, the discussions stay abstract; they ignore the details and complications one faces when implementing such algorithms on parallel or distributed hardware.
Most of the articles consider resource scheduling problems (e.g., Flow Shop Problems, Load Balancing Problems etc.) that are NP-hard in general. In practice, one relies on heuristics in order to find solutions that are "good enough". However, the fact that the presented heuristics are tailored to the specific problem they were developed for narrows the readership that can easily profit from these results. Only their general approach can be transferred to different problem domains.
Eight articles discuss aspects of genetic algorithms whence their general approaches are quite similar and there is some redundancy. The different priorities assigned by the respective papers are most likely significant only for readers interested in the specific problems discussed.
In the articles that study cellular automata and neural networks, readers without some background knowledge of the context where will not see how the results fit in. Thanks to their comprehensive bibliographies, they may serve as a starting point for further reading though.
Overall, I think this book fits well in an academic library where articles and textbooks for further reading are easily accessible. However, it is not suited for readers who want to learn how they can adapt the ideas of genetic algorithms etc. to their problem at hand.