The Interaction between Objectives and Constraints in Evolutionary Structural Engineering Optimisation

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{fenton:2013:USNCCM12,
  author =       "Michael Fenton and Ciaran McNally and 
                 Michael O'Neill",
  title =        "The Interaction between Objectives and Constraints in
                 Evolutionary Structural Engineering Optimisation",
  booktitle =    "12th U.S. National Congress on Computational Mechanics
                 (USNCCM12)",
  year =         "2013",
  editor =       "John Dolbow and Murthy Guddati",
  address =      "Raleigh, North Carolina, USA",
  month =        "22-25 " # jul,
  organization = "U.S. Association for Computational Mechanics",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Selection of appropriate techniques for handling
                 different constraints is a key part of evolutionary
                 optimisation in all disciplines. This also applies to
                 the field of Evolutionary Structural Engineering
                 Optimisation where multiple conflicting constraints are
                 present. These constraints include standard engineering
                 parameters such as stress, strain, deflection,
                 buckling, and weight; they can however also include
                 more complex constraints such as an accurate estimate
                 of the cost of the structure or a subjective assessment
                 of the architectural form. The selection of appropriate
                 functions for these constraints, and the subsequent
                 management of these parameters is a crucial part of the
                 evolutionary process. Structural engineering
                 optimisation will often require the designer to satisfy
                 multiple parallel objectives, and there may be overlaps
                 between both constraints and objectives. Understanding
                 the interaction between these constraints and the
                 overall individual fitness will therefore have a
                 significant impact on the quality of the designs
                 produced. As such, a key challenge for designers when
                 using evolutionary approaches is to find an accurate
                 metric that will allow the designer to: a) judge
                 individual constraints, and b) transform the
                 performance of the individual (relative to those
                 constraints) into a single coherent value for use by
                 the fitness function. The effect of differing
                 constraints on the overall population evolution is
                 noteworthy. It is shown that the addition of more
                 constraints does not necessarily reduce the search
                 space or improve the final population, but can help to
                 guide the search process where the search space is very
                 large. The differences between varying degrees of hard
                 and soft constraints are discussed, as are the
                 implications of their use in different scenarios. The
                 most appropriate methods of applying a costing
                 constraint to a structure are discussed, and
                 recommendations are made for which method to use.
                 Finally, the merits of both single-objective and
                 multiple-objective optimisation for evolutionary
                 structural engineering optimisation are compared and
                 contrasted.",
  notes =        "USNCCM12 is co-hosted by Duke University and North
                 Carolina State University. Other participating
                 institutions include Khalifa University of Science
                 Technology and Research (KUSTAR) and Army Research
                 Office, Statistical and Applied Mathematical Sciences
                 Institute (SAMSI). http://12.usnccm.org/",
}

Genetic Programming entries for Michael Fenton Ciaran McNally Michael O'Neill

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