Abstract

first, to understand how different solutions will affect the corresponding system, and then, to use this understanding to come up with the best solution. the knowledge that the humanity already has – which is usually described by experts, and the knowledge that we can extract from data.
Where does our understanding of different systems come from? There are two main sources of knowledge:
Expert knowledge is often described in terms of imprecise (“fuzzy”) words from natural language. To describe this knowledge in precise computer-understandable terms, we can use special technique designed specifically for this purpose – the technique of fuzzy logic.
To extract knowledge from data, we can use techniques designed specifically for this purpose – techniques of machine learning. Finally, to find the best solution, we use techniques designed specifically for this purpose – techniques of optimization.
There are many different machine learning and optimization techniques. One of the main sources of such techniques is to look for natural phenomena where knowledge is extracted from data and where optimization occurs. These processes occur both in live and in non-live matter, but processes in live matter are usually faster, thus easier to observe – we can see how an animal learns, we cannot easily see how a planet changes, it takes billions of years. So, a natural idea is to emulate biological processes.
Learning is usually performed in biological neurons within a living being – this motivated the current boom in neural networks. Optimization is more prominent not in an individual living being, but rather in the evolution of the living beings. Not surprisingly, many efficient optimization techniques come from emulating optimizing biological evolution. The corresponding evolutionary algorithms form, together with neural networks and fuzzy techniques, three parts of soft computing – as originally envisioned by Lotfi Zadeh and as implemented, e.g., in three-component biannual IEEE World Congresses on Computational Intelligence (WCCI).
It would be nice to have a faster example of an evolutionary process, where we do not need to guess what happened millions of years ago.
From this viewpoint, the book contains one main idea – and one auxiliary idea.
The main thesis of the book if that restrictions on mating between close relatives, while not very significant in biological terms, drastically speed up cultural evolution. The author traces the historical scientific and economic successes of the Western societies to the fact that in many of these societies, for obscure religious reasons, marriage between even distant relatives was not allowed. As a result, people could not marry within their own village, within their own clan – this broke the clan-ish structure. This boosted large-scale collaboration and trade – as opposed to more local village- and clan-level ones, and this was one of the main factors boosting Industrial Revolution, progress of science and engineering, etc.
So maybe it is worth trying to impose similar restrictions on “mating” (crossover) of individuals in genetic algorithms and evolutionary computations?
For non-scientists, the numerous graphs and correlations may make this book somewhat boring and unclear at places, but for us, it is fascinating. Enjoy – and maybe you will find some other ideas that can be used (or at least tried) in intelligent computing?
