## 04. Jig Optimisation (2)

**5.1.04. Jig Optimisation (2)**

**DESCRIPTION**

This exercise illustrates how Galapagos’ Evolutionary Solver can find optimal solutions for multiple variables using a gene pool. The exercise aims to find the optimal shape for a target curve to match the shape of multiple catenary curves. The solution is calculated by minimising the distance between the catenaries and the target curve.

**PROCEDURE**

1. A set of catenary curves is set as input

2. Selects the number of curves to be optimised

3. A Gene pool is set up to vary the shape of the target curve.

4. The slider “step per beam check” dictates how many locations on the target curve will be analysed on the target curve.

4. The slider “Proximity line resolution” dictates how many points on each catenary curve to calculate the deviation from the target curve’s shape.

5. The total distance between the two curves is set up as the “fitness” parameter of Galapagos.

This exercise is using Grasshopper version 1.0.0007

References: David Rutten, Evolutionary Principles applied to Problem Solving, https://www.grasshopper3d.com/profiles/blogs/evolutionary-principles, Accessed August 6, 2020.