Positional Effect of Crossover and Mutation in Grammatical Evolution

An often-mentioned issue with Grammatical Evolution is that a small change in the genotype, through mutation or crossover, may completely change the meaning of all of the following genes. This paper analyses the crossover and mutation operations in GE, in particular examining the constructive or destructive nature of these operations when occurring at points throughout a genotype. The results we present show some strong support for the idea that events occurring at the first positions of a genotype are indeed more destructive, but also indicate that they may be the most constructive crossover and mutation points too. We also demonstrate the sensitivity of this work to the precise definition of what is constructive/destructive

Genetic Programming with Gradient Descent Search for Multiclass Object Classification

Abstract. This paper describes an approach to the use of gradient descent search in genetic programming (GP) for object classification problems. Gradient descent search is introduced to the GP mechanism and is embedded into the genetic beam search, which allows the evolutionary learning process to globally follow the beam search and locally follow the gradient descent search. Two different methods, an online gradient descent scheme and an offline gradient descent scheme, are developed and compared with the basic GP method on three image data sets with object classification problems of increasing difficulty. The results show that both the online and the offline gradient descent GP methods outperform the basic GP method in both classification accuracy and training time and that the online scheme achieved better performance than the offline scheme.