This paper presents a study of different methods of using incremental evolution with genetic programming. Incremental evolution begins with a population already trained for a simpler but related task. No other systematic study of this method seems to be available. Experimental evidence shows the technique provides a dependable means of speeding up the solution of complex problems with genetic programming. A novel approach that protects against poor choices of problem simplifications is proposed, improving performance. Testing performed on tracking problems of multiple stages is analyzed.