Optimization with genetic algorithms and splines as a way for computer aided innovation: Follow up of an example with crankshafts Chapter in Scopus uri icon


  • This paper describes the conceptual foundations to construct a method on Computer Aided Innovation for product development. It begins with a brief recap of the different methodologies and disciplines that build its bases. Evolutionary Design is presented and explained how the first activities in Genetic Algorithms (GAs) helped to produce computer shapes that resembled a creative behavior. A description of optimization processes based on Genetic Algorithms is presented, and some of the genetic operators are explained as a background of the creative operators that are intended to be developed. A summary of some Design Optimization Systems is also explained and its use of splined profiles to optimize mechanical structures. The approach to multi-objective optimization with Genetic Algorithms is analyzed from the point of view of Pareto diagrams. It is discussed how the transition from a multi-objective optimization conflict to a solution with the aim of an ideal result can be developed means the help of TRIZ (Theory of Inventive Problem Solving), complementing the discipline of Evolutionary Design. Similarities between Genetic Algorithms and TRIZ regarding ideality and evolution are identified and presented. Finally, a brief presentation of a case study about the design of engine crankshafts is used to explain the concepts and methods deployed. The authors have been working on strategies to optimize the balance of a crankshaft using CAD and CAE software, splines, Genetic Algorithms, and tools for its integration [1] [2]. © 2008 International Federation for Information Processing.

Publication date

  • July 21, 2008