Quantifying and Interpreting the Effect of Intelligent Information Exchange Between Chromosomes in a Human Simulation of a Genetic Algorithm*

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.