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Generative processes and generative design approaches are topics of continuing interest and debate within the realms of architectural design and related fields. While they are often held up as giving designers the opportunity (the freedom) to explore far greater numbers of options/alternatives than would otherwise be possible, questions also arise regarding the limitations of such approaches on the design spaces explored, in comparison with more conventional, human-centric design processes. This article addresses the controversy with a specific focus on parametric-associative modelling and genetic programming methods of generative design. These represent two established contenders within the pool of procedural design approaches gaining increasingly wide acceptance in architectural computational research, education and practice. The two methods are compared and contrasted to highlight important differences in freedoms and limitations they afford, with respect to each other and to ‘manual’ design. We conclude that these methods may be combined with an appropriate balance of automation and human intervention to obtain ‘optimal’ design freedom, and we suggest steps towards finding that balance.

Multi-objective optimisation can help civil engineers achieve higher performance for lower costs in their designs. This is true whether 'performance' applies to structural strength or energy use, or whether 'cost' measures financial outlay or occupant satisfaction: if it can be quantified it can be optimised in some form. By exploring trade-offs between conflicting objectives and constraints, multi-objective optimisation enables informed decision-making. This paper outlines the principles and benefits of multi-objective optimisation and the means of implementation. The complementary aspects of parametric modelling and optimisation are discussed as an aid to the flexible design of buildings and structures. A range of real design problems are considered, including structural and environmental examples.

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