Now showing 1 - 5 of 5
  • Publication
    Examining the landscape of semantic similarity based mutation
    This paper examines how the semantic locality of a search operator affects the fitness landscape of Genetic Programming (GP). We compare the fitness landscapes of GP search when standard subtree mutation and a recently proposed semantic-based mutation, Semantic Similarity-based Mutation (SSM), are used. The comparison is based on two well-studied fitness landscape measures, namely, the autocorrelation function and information content. The experiments were conducted on a family of symbolic regression problems with increasing degrees of difficulty. The results show that SSM helps to significantly smooth out the fitness landscape of GP compared to standard subtree mutation. This gives an explanation for the better performance of SSM over standard subtree mutation operator.
      431Scopus© Citations 5
  • Publication
    Semantically-based crossover in genetic programming : application to real-valued symbolic regression
    We investigate the effects of semantically-based crossover operators in Genetic Programming, applied to real-valued symbolic regression problems. We propose two new relations derived from the semantic distance between subtrees, known as Semantic Equivalence and Semantic Similarity. These relations are used to guide variants of the crossover operator, resulting in two new crossover operators – Semantics Aware Crossover (SAC) and Semantic Similarity-based Crossover (SSC). SAC, was introduced and previously studied, is added here for the purpose of comparison and analysis. SSC extends SAC by more closely controlling the semantic distance between subtrees to which crossover may be applied. The new operators were tested on some real-valued symbolic regression problems and compared with Standard Crossover (SC), Context Aware Crossover (CAC), Soft Brood Selection (SBS), and No Same Mate (NSM) selection. The experimental results show on the problems examined that, with computational effort measured by the number of function node evaluations, only SSC and SBS were significantly better than SC, and SSC was often better than SBS. Further experiments were also conducted to analyse the perfomance sensitivity to the parameter settings for SSC. This analysis leads to a conclusion that SSC is more constructive and has higher locality than SAC, NSM and SC; we believe these are the main reasons for the improved performance of SSC.
      1476Scopus© Citations 177
  • Publication
    Semantic-based subtree crossover applied to dynamic problems
    Although many real world problems are dynamic in nature, the study of Genetic Programming in dynamic environments is still immature. This paper investigates the application of some recently proposed semantic-based crossover operators on a series of dynamic problems. The operators studied include Semantic Similarity based Crossover and the Most Semantic Similarity based Crossover. The experimental results show the advantage of using semantic based crossovers when tackling dynamic problems.
      425Scopus© Citations 4
  • Publication
    Grammar-based genetic programming : a survey
    Grammar formalisms are one of the key representation structures in Computer Science. So it is not surprising that they have also become important as a method for formalizing constraints in Genetic Programming (GP). Practical grammar-based GP systems first appeared in the mid 1990s, and have subsequently become an important strand in GP research and applications. We trace their subsequent rise, surveying the various grammar-based formalisms that have been used in GP and discussing the contributions they have made to the progress of GP. We illustrate these contributions with a range of applications of grammar-based GP, showing how grammar formalisms contributed to the solutions of these problems. We briefly discuss the likely future development of grammar-based GP systems, and conclude with a brief summary of the field.
      4263Scopus© Citations 248
  • Publication
    Improving the generalisation ability of genetic programming with semantic similarity based crossover
    This paper examines the impact of semantic control on the ability of Genetic Programming (GP) to generalise via a semantic based crossover operator (Semantic Similarity based Crossover - SSC). The use of validation sets is also investigated for both standard crossover and SSC. All GP systems are tested on a number of real-valued symbolic regression problems. The experimental results show that while using validation sets barely improve generalisation ability of GP, by using semantics, the performance of Genetic Programming is enhanced both on training and testing data. Further recorded statistics shows that the size of the evolved solutions by using SSC are often smaller than ones obtained from GP systems that do not use semantics. This can be seen as one of the reasons for the success of SSC in improving the generalisation ability of GP.
      662Scopus© Citations 33