The Interaction between Objectives and Constraints in Evolutionary Structural Engineering Optimisation

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

@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/ http://12.usnccm.org/technical-program",

}

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

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