- Terry P. Riopka, Mona T. Diab, Peter Book. 2000. Quantifying and Interpreting the Effect of Intelligent Information Exchange Between Chromosomes in a Human Simulation of a Genetic Algorithm. Abstract: A genetic algorithm is simulated using human bein gs as "chromosomes" in a preliminary study intended to quantify and interpre t 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 ru n for each combination of factors as well as a machine simulation for comparison. Qualit ative analysis of recorded conversations indicate extensive use of memory and development of block biases during genetic algorithm evolution. Informal analysis shows that genetic alg orithm simulations using complex data manipulations combined with exact knowledge of string fitnesses seem to out-perform a standard machine implementation for the given optim ization fitness function. Interestingly, polar combinations: simple data manipulation/minimum information and complex data manipulation/maximum information simulations seem to out-perform other combinations. A genetic algorithm can be interpreted as a process of simulated evolution in which chromosome strings gradually modify the information encoded in their s chemata. Some form of replication based on string fitness is typically used to simulate natural selec tion and incorporate the Darwinian notion of surviv al of the fittest. In addition, strings are altered accordin g to the probabilistic rules of various operators, of which crossovers and mutations are the most well known. In this information exchange phase, the strings (heretofore referred to as chromosomes) exchange symbols probabilistically to try to impr ove their fitness in order to survive through to the following generatio n. A building block hypothesis has been suggested but not theoretically proven to be the primary impetus for solution improvement over generations. Efforts to develop analytical methods to determine the efficac y of problem coding/operator combinations have met with limited success but, nevertheless, strongly su pport this contention. According to the hypothesis , chromosomes in the genetic population exchange varying sized blocks of consecutive symbols which, over time, tend to proliferate in the components of chro mosomes that survive from one generation to the nex t. In current implementations of genetic algorithms, the application of the various operators is probabilist ic. In most cases these probabilities are chosen based on empirical studies and heuristics and are often non- optimal for any given application. An alternative mechanism is proposed which uses memory and logical reasoning to determine how information is exchanged between chromosomes. If memory, logical reasoning and more complex data manipulation could be exploited to streamline the s earch for these building blocks, the efficacy of th e genetic algorithm search might be improved and coul d also lead to more robust automated genetic algori thm
