Improving Engineering Design Models Using An Alternative Genetic Programming Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{watson:1998:ACDM,

• author = "Andrew H. Watson and Ian C. Parmee",
• title = "Improving Engineering Design Models Using An Alternative Genetic Programming Approach",
• booktitle = "Adaptive Computing in Design and Manufacture",
• year = "1998",
• editor = "Ian C. Parmee",
• pages = "193--206",
• address = "Plymouth, England",
• month = "21-23 " # apr,
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4471-1589-2",
• URL = "http://link.springer.com/chapter/10.1007/978-1-4471-1589-2_15",
• DOI = "doi:10.1007/978-1-4471-1589-2_15",
• abstract = "This paper describes an alternative approach to Genetic Programming (GP) for engineering design model development. The algorithm is initially developed to solve Boolean induction and simple symbolic regression problems within a discrete search space. This technique, called DRAM-GP (i.e. Distributed, Rapid, Attenuated Memory GP), is based upon a steady state population using a novel constrained complexity crossover operator. Node complexity weightings are introduced to provide a basis for speciation. Separate species of solutions, classified by complexity can be established which act as discrete GP sub-populations communicating with each other via crossover. The technique is extended to incorporate both continuous and discrete search spaces (HDRAM-GP i.e. Hybrid DRAM-GP). HDRAM-GP includes a real numbered Genetic Algorithm (GA) to aid search in the continuous space. Its application is demonstrated on engineering fluid dynamics systems.",

}

Genetic Programming entries for Andrew H Watson Ian C Parmee

Citations