Learning from natural evolution

When body armor is considered, no matter if it is for humans or animals, its ultimate objective is clear maximize survivability. This objective may be also reformulated as a technical contradiction in accordance with TRIZ (see Chapter 9) maximize body protection and maximize mobility. Since the same force (maximization of survivability) drives the evolution of both kinds of body armor, knowledge about animal body armor evolution is relevant to designing human body armor. It may be an oversimplification of a 

In nature, various different species use plate armor for protection, with differentiated adaptations for movement. Surprisingly, in nature, separate lines of evolution have emerged, each focused only on a specific requirement maximization of protection, maximization of mobility, or finding a balance between protection and mobility. 

The study of evolution of turtles has revealed several design heuristics that are directly applicable to the development of new types of human body armor  

The third heuristic, Create multilayered body armor, can be explained by the fact that combining materials may create unique features of armor impact damping, thermal insulation, penetration resistance, lightness, and so on. The fourth heuristic, Introduce shock-absorbing layers, only adds specificity and sophistication to the previous one and reminds inventors that impact protection is extremely important and that this can be improved using a layer of fat, for example, to protect a brain against impact. 

Heuristic No. 5, Minimize weight, can be understood as concern about mobility, even among turtles and particularly among humans, but it is also about minimizing the energy and food required to build and maintain heavy armor. Finally, the last heuristic. No. 6, Maximize articulation, is 

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Bionanotechnology also aims to learn from nature — to understand the structure and function of biological devices and to utilize nature s solutions to advance science and engineering. Evolution has produced... 

Esterases have a catalytic function and mechanism similar to those of lipases, but some structural aspects and the nature of substrates differ [4]. One can expect that the lessons learned from the directed evolution of lipases also apply to esterases. However, few efforts have been made in the directed evolution of enantioselective esterases, although previous work by Arnold had shown that the activity of esterases as catalysts in the hydrolysis of achiral esters can be enhanced [49]. An example regarding enantioselectivity involves the hydrolytic kinetic resolution of racemic esters catalyzed by Pseudomonasfluorescens esterase (PFE) [50]. Using a mutator strain and by screening very small libraries, low improvement in enantioselectivity was... 

In Darwin s times heredity was a mystery, but this did not prevent him from concluding that natural selection works on heritable variations. All that he needed to know about heredity were the two facts that he learned from breeders, namely that (1) every individual in a population has unique characteristics, and (2) many distinctive traits are inherited. The discovery of the hereditary mechanism could not cancel these experimental facts, and could not therefore deny natural selection. That discovery, however, could reveal new mechanisms of evolution, and reduce the role that natural selection played in the history of life. This is why the study of heredity came to be seen as the testing ground for any evolutionary theory, and for almost a century, in fact, the debate on evolution has largely been a debate on genetics. 

The structures of the products of nature continue to intrigue organic chemists. What are the mechanisms by which they are produced and how has evolution influenced their persistence How can we take advantage of these compounds and their functions In particular, can we use these natural products and the chemistry that we learn from them to improve human and veterinary health ... 

EAs repeatedly carve up old members of the population to create fresh solutions. As in natural selection, competition within the population is essential, otherwise its evolution would be unpredictable and undirected, the algorithm would be as likely to retain poor solutions as promising ones and would make a lengthy and probably unproductive random walk over the search surface. Since individuals in the current population have evolved from those created in past generations, they reflect some of the lessons learned during previous attempts at solution. It is in this fashion that the algorithm learns about a problem. 

Holland s book [10], published in 1975, is generally acknowledged as the beginning of the research of GA. The GA is a model of machine learning which derives its behavior from a metaphor of the processes of evolution in nature [8]. Since their introduction and subsequent popularization, the GA have been frequently used as an alternative optimization tool to the conventional methods and have been successfully applied to a variety of areas, and find increasing acceptance [23]. 

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