An Incremental Genetic Algorithm for Real-Time Learning

Abstract

The genetic algorithm, operated in batch mode, evaluates the whole population in some environment and generates through selection, crossover and mutation a new population. In a real-time learning situation, where the population can only be evaluated sequentially, much of the computation and all of the learning is 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. This paper introduces an incremental genetic algorithm which generates only one new member of the population and deletes only old one at a time thus equalizing the amount of computation and learning at each time interval. It then compares the performance of the incremental and non-incremental genetic algorithms and of a rule based system for optimising combustion on ten simulations of multiple burner installations.