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Without stringent enumeration, guide random search technique (GRST) methods are attempting to seek a whole feasible design field and, in principle, have a global optimum. If this optimum is not suitable internationally, then traditional exploration procedures provide no underlying means to move away from the optimal local field to continue the search for the optimum global setting. However, it should be borne in mind that there can be no confidence that a GRST algorithm can solve a complex design problem globally and, as mentioned elsewhere in the book, no answer can be challenged to ensure that a global solution has been discovered. The methods, though, are rigorous and usually will include a solution that significantly improves on any initial concept put forward by the design team.

GRST methods can deal with problems with the architecture of undistinguished functions and with many local improvements. The ability to deal with non-differentiable functions makes it easy to address problems related to distinct design variables, which are common aspects of structural design. Many GRST methods are well adapted for parallel processing, in particular the evolutionary algorithms mentioned in the next section. The number of implementing variables would allow concurrent processing to be used to respond within a reasonable period if every MDO problem is resolved by the GRST method rather than trivial.

Evolutionary algorithms are a subset of GRST techniques that employ very special approaches that focus on evolutionary concepts seen in nature. This approach also exposes some designs to spontaneous variations and offers anyone with a practical advantage an increased opportunity to produce “spring” designs. There are a number and different methods to solving complicated optimization problems using the same straightforward probabilistic technique. We are concerned with GA, which may be the most popular evolutionary form of algorithms in-process libraries or in commercial MDO systems.