Journal of Computer Science & Systems Biology

Chris Headleand

Publications

• Research Article
  Swarm Based Population Seeding of Grammatical Evolution
  Author(s): Chris Headleand and William J TeahanChris Headleand and William J Teahan
  
  Evolutionary Algorithms, although powerful, are known to be wasteful and time consuming, requiring the evaluation of a large number of candidates. However the strength of the methodology is their ability to continually optimise the population hopefully ensuring a near optimal final solution. When applied to automatic programming tasks, the same limitations are observed, notably the time taken to develop a solution. An alternate, swarm-based method ‘Grammatical Herding’ suffers from the opposite concerns. Whilst it generates moderate fitness solutions quickly, these candidates often lack the optimisation of solutions generated via an evolutionary approach. This study details a hybrid technique ‘Seeded Grammatical Evolution’ where Grammatical Herding (GH) is used to seed the initial population of a Grammatical Evolution (GE) algorithm.. Read More»
  DOI: 10.4172/jcsb.1000110

  Abstract PDF