An incremental genetic algorithm for real-time optimisation

The genetic algorithm, operated in batch mode, evaluates the whole population in some environment and generates a new population through selection, crossover, and mutation. In a real-time learning situation, where the population can be evaluated only sequentially, much of the computation and all of the learning is thus concentrated into one time interval between the evaluation of the last member of the old population and the generation of the first member of the new. The author describes how the genetic algorithm can be operated in interactive mode, generating only one new member of the population and deleting only one old one at a time, thus equalizing the amount of computation and learning at each time interval. He then compares the performance of the two modes of operating the algorithm and of a rule-based system for optimizing combustion on ten simulations of multiple burner installations, giving a statistical analysis of the results obtained