GENETIC SEARCH - AN APPROACH TO THE NONCONVEX OPTIMIZATION PROBLEM

Principles of genetics and natural selection are adapted into a search procedure for function optimization. Such methods are based on a randomized selection from that restricted region of the design space that yields an improvement in the objective function. Their lack of dependence on function gradients makes these methods less susceptible to pitfalls of convergence to a local optimum. An implementation of the approach to a class of problems in structural optimization with demonstrated nonconvexities or disjointness is discussed in the paper. These examples suggest the effectiveness of the proposed method for such problems. The principal drawback of the method is an increase in function evaluations necessary to locate an optimum. Possible strategies to overcome this limitation are presented. © 1990 American Institute of Aeronautics and Astronautics, Inc., All rights reserved.