Abstract-Crossover forms one of the core operations in genetic programming and has been the subject of many different investigations. We present a novel technique, based on semantic analysis of programs, which forces each crossover to make candidate programs take a new step in the behavioural search space. We demonstrate how this technique results in better performance and smaller solutions in two separate genetic programming experiments.Index Terms-Genetic programming, program semantics, crossover, reduced ordered binary decision diagrams.

Abstract-Using semantic analysis, we present a technique known as semantically driven mutation which can explicitly detect and apply behavioural changes caused by the syntactic changes in programs that result from the mutation operation. Using semantically driven mutation, we demonstrate increased performance in genetic programming on seven benchmark genetic programming problems over two different domains.Index Terms-Genetic programming, program semantics, semantically driven mutation, reduced ordered binary decision diagrams.

Population initialisation in genetic programming is both easy, because random combinations of syntax can be generated straightforwardly, and hard, because these random combinations of syntax do not always produce random and diverse program behaviours. In this paper we perform analyses of behavioural diversity, the size and shape of starting populations, the effects of purely semantic program initialisation and the importance of tree shape in the context of program initialisation. To achieve this, we create four different algorithms, in addition to using the traditional ramped half and half technique, applied to seven genetic programming problems. We present results to show that varying the choice and design of program initialisation can dramatically influence the performance of genetic programming. In particular, program behaviour and evolvable tree shape can have dramatic effects on the performance of genetic programming. The four algorithms we present have different rates of success on different problems.