Evolutionary algorithm for structural optimization
Mark S. Voss, Christopher M. Foley · 1999
A hybrid rank-based evolutionary algorithm that takes advantage of a-priori problem specific information and operates on a high cardinality heuristic genetic representation is presented in this paper. A rank based fitness statement combined with generationally dependant penalty exponents is proposed to condition the seven components of the fitness statement so they participate fairly during the evolutionary process. Translocation crossover and intelligent mutation were utilized to maintain genetic diversity. A graphical method is proposed to monitor the progress of the components of the fitness function, allowing the user to interact with the evolutionary process. Generationally dependant non-linear rank based selection was used to orchestrate a soft landing near the global optimum for an example problem with 20 discrete design variables. 1 INTRODUCTION The genetic algorithm can be classified as a stochastic procedure and its success depends on the algorithm's ability to effectively s...