A genetic algorithm is simulated using human beings as "chromosomes"
in a preliminary study intended to quantify and interpret the effect
of intelligent information exchange on genetic algorithm
performance. Two factors are varied: the amount of information
supplied to the cohort and the type of data manipulation allowed
during the exchange. A human simulated genetic algorithm is run for
each combination of factors as well as a machine simulation for
comparison. Qualitative analysis of recorded conversations indicate
extensive use of memory and development of block biases during genetic
algorithm evolution. Informal analysis shows that genetic algorithm
simulations using complex data manipulations combined with exact
knowledge of string fitnesses seem to out-perform a standard machine
implementation for the given optimization fitness
function. Interestingly, polar combinations: simple data
manipulation/minimum information and complex data manipulation/maximum
information simulations seem to out-perform other combinations.
(Also cross-referenced as UMIACS-TR-2000-38, LAMP-TR-045)