{'fraction_non_alphanumeric': 0.0196078431372549, 'fraction_numerical': 0.001912960306073649, 'mean_word_length': 5.436923076923077, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Investigation of genetic algorithm design representation for multi-objective truss optimization

Investigation of Genetic Algorithm Design Representation for Multi-Objective Truss Optimization. (August 2006) Soumya Sundar Pathi, B.Tech., National Institute of Technology Calicut, India Chair of Advisory Committee: Dr. Anne Raich The objective of this research is to develop a flexible design grammar and genetic algorithm representation to be used in a multi-objective optimization method to design efficient steel roof trusses given space dimensions and loading requirements by the user. The goal of implementing the method as a multi-objective problem is to obtain a set of near-optimal trusses for the defined unstructured problem domain, not just a single near-optimal design. The method developed was required to support the exploration of a broad range of conceptual designs before making design decisions. Therefore, a method was developed that could define numerous design variables, support techniques to locate global or near-global optimal designs, and improve the efficiency of the computational procedures implemented. This research effort was motivated by the need to consider structural designs that may be beyond the established conventions of designers in the search for cost-efficient, structurally-sound designs. An effective design grammar that is capable of generating stable trusses is defined in this research. The design grammar supports the optimization of member size, in addition to truss geometry and topology. Multi-objective genetic algorithms were used to evolve sets of Pareto-optimal trusses that had varying topology, geometry, and member sizes. The Pareto-optimal curves provided design engineers with a range of near-optimal design alternatives that showed the tradeoffs that occur in meeting the stated objectives. Designers can select their final design from this set based on their own individual weighting of the design objectives. Trials are performed using a multiobjective genetic algorithm that works with the design grammar to evolve trusses for