Evolutionary Computing (EC)

Evolutionary Computing (EC) is a method in the field of Artificial Intelligence. EC provides methods of search for the optimium by the means of randomization, mutation, recombination and selection. It mimics and learns from the biological evolution.

Similarities (compared to the biological evolution):

• each subject is defined by a highly abstract code (genotype),


• each subject is grown from genotype into phenotype (morphogenesis),


• the probability for each subject to survive depends on its fitness; environment plays a large role in that,


• the variety of the genetic pool of the population is ensured by recombination, providing offspring a new genotype, derived from genotypes of its ancestors,


• further variety is achieved by random mutation of genotype.

Differences (comapred to the biological evolution):

• generation turnover rate can be much higher - instead of hours, days or years, sequences of generations can be developed in miliseconds, evolution can run faster,


• flexibility in the rules of inheritance - for example, in nature a subject typically inherits genetic material from 2 ancestors; these limitations can be easily overcome (e.g. a subject can have 5 ancestors),
  
• it is not possible to simluate the complexity of natural systems in an artificial enviroment (for example, the human brain has at least 10-100 billion (short scale - 10⁹) neurons; each of them is capable of "parallel processing" and is connected to others through about 1.000-10.000 synapses; these estimates compute to 10¹³ to 10¹⁵ number of synapses...); and we're not talking just about data storing, but (parallel) information processing as well.

So, how to apply that stuff in architecture? The plan is to grow a population of "houses"; each house would be based on its encoded genotype (blueprint), from which a real house would be grown. The houses would mate and produce offspring, which would be assessed; the subject with the greatest fitness (most appropriate house) would survive and pass its genetic material to the next generation. After many generations, best designs should emerge. Huh. I admit it's a long shot, but certainly worth a try :-)