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Design Automation usually refers to electronic design automation, or Design Automation which is a Product Configurator. Extending Computer-Aided Design (CAD), automated design and Computer-Automated Design (CAutoD)[1][2][3] are more concerned with a broader range of applications, such as automotive engineering, civil engineering,[4][5][6][7] composite material design, control engineering,[8] dynamic system identification and optimization,[9] financial systems, industrial equipment, mechatronic systems, steel construction,[10] structural optimisation,[11] and the invention of novel systems.[12]

The concept of CAutoD perhaps first appeared in 1963, in the IBM Journal of Research and Development,[1] where a computer program was written.

1. to search for logic circuits having certain constraints on hardware design
2. to evaluate these logics in terms of their discriminating ability over samples of the character set they are expected to recognize.

More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically-inspired machine learning,[13] including heuristic search techniques such as evolutionary computation,[14][15] and swarm intelligence algorithms.[16]

## Guiding designs by performance improvements

Interaction in computer-automated design

To meet the ever-growing demand of quality and competitiveness, iterative physical prototyping is now often replaced by 'digital prototyping' of a 'good design', which aims to meet multiple objectives such as maximised output, energy efficiency, highest speed and cost-effectiveness. The design problem concerns both finding the best design within a known range (i.e., through 'learning' or 'optimisation') and finding a new and better design beyond the existing ones (i.e., through creation and invention). This is equivalent to a search problem in an almost certainly, multidimensional (multivariate), multi-modal space with a single (or weighted) objective or multiple objectives.

## Normalized objective function: cost vs. fitness

Using single-objective CAutoD as an example, if the objective function, either as a cost function ${\displaystyle J\in [0,\infty )}$, or inversely, as a fitness function ${\displaystyle f\in (0,1]}$[1] where a computer program was written.

More recently, traditional CAD simulation is seen to be transformed to CAutoD by biologically-inspired machine learning,[13] including heuristic search techniques such as evolutionary computation,[14][15] and swarm intelligence algorithms.[16]