Genetic Algorithms as a Computational Theory of Conceptual Design

The essentially inductive processes of conceptual design have received scant attention in those portions of the design literature concerned with effective computation or mathematical rigor. This paper draws a connection between the discriminative and recombinative processes of conceptual design and genetic algorithms (GAs)—search procedures based on the mechanics of natural genetics and natural selection. Recent empirical and theoretical results with a type of GA called a messy genetic algorithm support the conjecture that GAs can solve all problems no harder than the functions of bounded deception in time that grows no more quickly than a polynomial function of the number of decision variables. These results suggest that inductive designers—far from wasting their efforts when they bet on some combination of past designs—are engaging in a computationally effective means of solving very difficult, even misleading, design problems. Although more theoretical and computational work is needed, the paper shows one path to a more rigorous theory of conceptual design, a path that should help put design on the same mathematical foundations as analysis without detracting from the joy, or the necessity, of human invention.