A comparison of simulated evolution and genetic evolution performance

Simulated evolution (SE) and the genetic algorithm (GA) are closely related methods for finding optimal solutions by directed random search. Both methods start with a population of randomly selected trial solutions and use that information to “evolve” a next generation of trials which, on the average, has improved fitness (i.e., is closer to the optimum). As the population average improves, so too, is the best of the population likely to be closer to the optimal solution sought. The methods differ principally in the manner by which each new generation evolves from the previous generation. In addition, the GA represents the search space by discrete points so that the optimum is limited to one of those points. The SE method treats the search space as continuous. The paper makes a side-by-side comparison of the performance of the two methods when applied to the same problem. The problem to be solved is sometimes called the Dictator Problem. It is to maximize the minimum angular separation, α among n points placed on a sphere