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So, I've been toying with genetic algorithms a bit. Making a genetic algorithm basically involves making a list of responses to every situation the algorithm might encounter. Typically you start with a random pool of algorithms, compete them, mutate and mix and match the winners' genomes to make a new generation, and then repeat indefinitely. (Does this process sound familiar?) The idea of encoding a response for every possible situation works well for some problems, but not for others. For instance, it would never work for teaching a computer to play go. ( Why not? )
May. 30th, 2011
complexity 2 PS: more big numbers
May. 30th, 2011 11:59 pmThe size of the human haploid genome (one of the two genome copies each person contains in each of their 10 trillion cells) is about 3 billion bases. Each base can be one of four, so the total possible number of DNA sequences of 3 billion base pairs is 43,000,000,000, which is about 1 x 101,800,000,000. You, snowflake, constitute one of those. Well, two, actually. (Not counting all the varied mutations I'm sure you've picked up on the way.)
While genetic algorithms are cool, this does point to a limitation as far as considering them as models of biological evolution- they're not going to do well with a 3-billion-value genome. The number of actual "genes" is something like 30,000, not counting the extra variation you get from alternative splicing, and the number of theoretically possible identities of each of those DNA stretches is... very large. Our genome is rather different from the genome of a workable genetic algorithm in other ways, too. It is not just a list of predetermined responses to a set of predetermined stimuli. It is instructions for coding an information processor. For example, those 3 billion base pairs contain instructions for building a brain containing 100 billion neurons. This kind of hierarchy (DNA bases -> genes -> cells -> organs -> organisms -> societies) is a hallmark of truly complex systems. If we think of each neuron as a bit (either on or off, 0 or 1), the number of possible brain states is 2100,000,000,000, which is about 1030,000,000,000. One of those brain states, snowflake, is exactly what you're thinking and feeling right now.
While genetic algorithms are cool, this does point to a limitation as far as considering them as models of biological evolution- they're not going to do well with a 3-billion-value genome. The number of actual "genes" is something like 30,000, not counting the extra variation you get from alternative splicing, and the number of theoretically possible identities of each of those DNA stretches is... very large. Our genome is rather different from the genome of a workable genetic algorithm in other ways, too. It is not just a list of predetermined responses to a set of predetermined stimuli. It is instructions for coding an information processor. For example, those 3 billion base pairs contain instructions for building a brain containing 100 billion neurons. This kind of hierarchy (DNA bases -> genes -> cells -> organs -> organisms -> societies) is a hallmark of truly complex systems. If we think of each neuron as a bit (either on or off, 0 or 1), the number of possible brain states is 2100,000,000,000, which is about 1030,000,000,000. One of those brain states, snowflake, is exactly what you're thinking and feeling right now.