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Evolutionary algorithms, multi-objective optimization, design space exploration, fitness function design, population-based methods, and generative workflows for AEC computational design

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# Generative Design for AEC Computational Design

## 1. Generative Design Paradigm

### 1.1 Definition and Scope

Generative design is a computational design methodology in which a designer defines a problem through goals, constraints, and variable parameters, and an algorithmic system autonomously generates, evaluates, and evolves candidate solutions across a defined design space. Unlike traditional design where the human produces every solution manually, generative design shifts the designer's role from direct form-maker to curator of outcomes — defining *what* is desired rather than *how* to achieve it.

In the AEC context, generative design applies to problems ranging from single-building floor plan layouts and structural topologies to neighborhood-scale massing studies and infrastructure routing. The common thread is a design space too large for exhaustive manual exploration.

### 1.2 Distinction from Parametric Design

The confusion between parametric and generative design is pervasive. The distinction is fundamental:
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