Tracing Engineering Evolution with Evolutionary Algorithms

The mankind achieved an astonishing technological development through centuries of innovation, creation and continuous improvement. The history of engineering is the inherent component of the civilization. Moreover, outstandingly important lessons for further development can be studied in the history of engineering. The investigations of the recent complex engineering knowledge, experience, analytical and computational tools may serve to explain the technical progress and facilitate the future development. For this purpose this chapter will present how the evolutionary algorithms can simulate the developing complexity of engineering reasoning that in reverse can back-trace the primitive origins of modern technical products. The chapter will resume the evolutionary algorithms as well as the evolutionary optimization and design processes based on innovative and creative activities with the aim to define their potentialities in discovering the evolution of engineering products. More so when adding to the whole process the touch of randomness introduced in form of mutation operator the algorithm gains the property to converge to the global optimum within multi-modal search space. Both of these processes crossover and mutation have been present in the natural evolution for eons of time. From an algorithmic perspective crossover and mutation enable adaptation of the population of feasible solutions to the imposed environment conditions of the search spaces. The chapter will concentrate on the multi-objective optimization problems taking for example the NSGA-II algorithm (Deb, 2001). The result will be obtained as the population of optimal solutions distributed along the Pareto frontier. Using constraint domination condition and constrained tournament selection operator the evolution of the object under consideration will be explained. Normally engineering relies on the design process that is for technical purposes modelled as a set of cyclic activities put in a logical order to guide the procedure until the desired technical aim is reached. The design process is comprehensible as a shortcut to a satisfying product that is also in clear correlation with the formulation of an algorithm, particularly with evolutionary algorithm. The evolutionary methods affect design process and teach about process itself. They stimulate innovation and creativity during the human efforts to design and apply processes which are all in reality an attempt to produce an unbiased human performed heuristic search. The evolutionary design relies on the normal contemporary progressive engineering reasoning aspired with achievement of highly efficient products providing appropriate safety levels by employing genetic algorithms. The chapter will consider how the reverse process to the technical progress can reconstruct the origins of contemporary products using evolutionary