Evolution is agnostic about what to develop - it has no design goals [Hop82, p. 2254] - but qualities do emerge that have co-varied with the prospects of persistence of this quality. The prospect of persistence is normally related to the reproductive and survival prospects of the individuals in a population [Gea05, p. 99]. In artificial systems it is determined by the fitness function. It is critical to note that evolution and artificial evolution succeed in finding a solution to problems without explicitly giving it knowledge on how to solve the problem and regardless of what the problem is.
Evolutionary algorithms are often employed to solve optimization problems which are otherwise computationally prohibitive/expensive (np-hard and np-complete problems). While they are useful in this respect, it greatly undervalues their potential of being able to develop solutions to any challenge.
Two important observations:
- EAs have no trouble designing the complex circuits that may be required
Hopfield: Our understanding of such simple circuits in electronics allows us to plan larger and more complex circuits which are essential to large computers. Because evolution has no such plan, it becomes relevant to ask whether the ability of large collections of neurons to perform "computational" tasks may in part be a spontaneous collective consequence of having a large number of interacting simple neurons.
Whereas in conventional computers synchronization of the digital building blocks is achieved using a clock signal, there is no such global clock in biological systems. In a more biologically oriented simulation, global synchronization should thus be avoided [Roj96].
Erik de Bruijn 2007-10-19