2016/01/02 by Darren M. Chitty, Chitty, Darren M.
Biochemistry, Genetics and Molecular Biology · Computer Science · #CRISPR and Genetic Engineering #Distributed #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Parallel #and Cluster Computing (cs.DC) #cs.DC
paper · pdf · doi:10.48550/arxiv.1601.00221
arxiv created 2016/01/02 · openalex publication_date 2016/01/02 · arxiv updated 2016/01/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Genetic Programming (GP) is a computationally intensive technique which also has a high degree of natural parallelism. Parallel computing architectures have become commonplace especially with regards Graphics Processing Units (GPU). Hence, versions of GP have been implemented that utilise these highly parallel computing platforms enabling significant gains in the computational speed of GP to be achieved. However, recently a two dimensional stack approach to GP using a multi-core CPU also demonstrated considerable performance gains. Indeed, performances equivalent to or exceeding that achieved by a GPU were demonstrated. This paper will demonstrate that a similar two dimensional stack approach can also be applied to a GPU based approach to GP to better exploit the underlying technology. Performance gains are achieved over a standard single dimensional stack approach when utilising a GPU. Overall, a peak computational speed of over 55 billion Genetic Programming Operations per Second are observed, a two fold improvement over the best GPU based single dimensional stack approach from the literature.