Showing posts with label evolutionary computing. Show all posts
Showing posts with label evolutionary computing. Show all posts
Thursday, April 10, 2008
Spotted: Evolved Virtual Creatures
This is a must-see for anyone interested in Evolutional Computing (EC). Actually, it's a must-see for everyone.
Thursday, March 20, 2008
Evolutionary Computing Applications in Architecture design
While evolutionary computing (EC) is nothing more than a search for the optimum, so is every (and thus also architectural) design. How can EC be applied for usage in architecture?
As Vitruvius pointed out some two thousand years ago, a well designed architecture has to match three standards: firmness (structural integrity), commodity (functionality) and delight (aesthetic value).
So, in the search for optimal architecture, it is very logical to map each of the Vitruvius' standards to evolutionary computing's fitness function.
When asssessing the structural fitness of a subject (a building), the fittest are those capable of carrying more static stress (burden), preferably with less material used for the construction.
In a similar way, the functionality can be assessed based on the set of conditions. It is certanly better if the drawing room is bigger than the hall; and it's better that master bedrom is closer to the bathroom than to the kitchen.
The hardest thing to process for the computer is the aesthetic. While one could struggle to automate the aesthetic assessment process with certain mathematic rules (symetry, proportions, rythm....), I remain sceptic about such an approach - aesthetic is a highly subjective category.
But even if the fitness function for aesthetic cannot be calculated, that does not mean evolutionary computing cannot be applied in that field.
If aesthetic is a subjective quality, something in the eye of the beholder - the human being, then a real human being must perform the assessment of the subjects. Selection by fitness function still exists, but it is not automated nor calculated. A human being, artist/architect in our case, would make selections in the manner of a horse breeder. Some subjects in population would look better and would be favored for passing their properties to the next generations.
One might argue that human intervention would render EC unusable. Well, it might slow down the process (few seconds instead of few miliseconds - that's 1000x slower), but all the key elements of the EC remain. Only the fitness function for the aesthetic is admitted to be what it actually is - inevitably a human's judgement.
As Vitruvius pointed out some two thousand years ago, a well designed architecture has to match three standards: firmness (structural integrity), commodity (functionality) and delight (aesthetic value).
So, in the search for optimal architecture, it is very logical to map each of the Vitruvius' standards to evolutionary computing's fitness function.
When asssessing the structural fitness of a subject (a building), the fittest are those capable of carrying more static stress (burden), preferably with less material used for the construction.
In a similar way, the functionality can be assessed based on the set of conditions. It is certanly better if the drawing room is bigger than the hall; and it's better that master bedrom is closer to the bathroom than to the kitchen.
The hardest thing to process for the computer is the aesthetic. While one could struggle to automate the aesthetic assessment process with certain mathematic rules (symetry, proportions, rythm....), I remain sceptic about such an approach - aesthetic is a highly subjective category.
But even if the fitness function for aesthetic cannot be calculated, that does not mean evolutionary computing cannot be applied in that field.
If aesthetic is a subjective quality, something in the eye of the beholder - the human being, then a real human being must perform the assessment of the subjects. Selection by fitness function still exists, but it is not automated nor calculated. A human being, artist/architect in our case, would make selections in the manner of a horse breeder. Some subjects in population would look better and would be favored for passing their properties to the next generations.
One might argue that human intervention would render EC unusable. Well, it might slow down the process (few seconds instead of few miliseconds - that's 1000x slower), but all the key elements of the EC remain. Only the fitness function for the aesthetic is admitted to be what it actually is - inevitably a human's judgement.
Monday, March 17, 2008
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):
Differences (comapred to the biological evolution):
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 :-)
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 :-)
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