Cartesian Genetic Programming for Truss Optimization

This repository contains the Python implementation for the paper: EvoDevo: Bioinspired Generative Design via Evolutionary Graph-Based Development as part of RIED project. It specifically focuses on the Cartesian Genetic Programming (CGP) methods discussed in the paper for optimising the design of truss structures.

📜 About the Project

This work presents a bio-inspired generative design algorithm that uses the concept of evolutionary development (EvoDevo). Instead of directly optimising a design, this approach evolves a set of reusable developmental rules.

The core of this system is an artificial Gene Regulatory Network (GRN), which acts as a controller within simple entities called "cells". For a truss structure, these cells represent the nodes (vertices) and edges (members). Each cell's GRN senses its local environment (e.g., strain energy, volume) and outputs a "growth" command, such as moving a node or changing an edge's cross-sectional area.

This repository implements the CGP-based GRN, which offers more interpretable, "white-box" outputs compared to neural network alternatives like GNNs. The goal is to evolve a controller that can effectively optimise a truss structure over a series of developmental steps.

✨ Key Features

• Evolutionary Development (EvoDevo): An indirect approach to design where the "designer" (the GRN) is evolved, not the design itself.
• Cartesian Genetic Programming (CGP): A graph-based evolutionary algorithm used to create readable and efficient GRN controllers.
• Cellular Representation: The truss is broken down into node and edge cells, each with its own controller that makes local decisions to achieve a global objective.
• Multiple Growth Mechanisms: The system can optimise trusses by:
  • Adjusting the cross-sectional area of edges (edge-only method).
  • Moving the coordinates of nodes (node-only method).
  • Doing both simultaneously (node-edge method).
• Fitness Function: The evolutionary algorithm optimises controllers based on a fitness score that combines the total strain energy and total volume of the truss.

🔧 Getting Started

Prerequisites

This project uses Conda to manage its environment and dependencies. You'll need to have Anaconda or Miniconda installed.

Installation

1. Clone the repository:

   git clone [https://github.com/Tahernezhad/Cartesian-Genetic-Programming-for-Truss-Optimization.git](https://github.com/Tahernezhad/Cartesian-Genetic-Programming-for-Truss-Optimization.git)
   cd Cartesian-Genetic-Programming-for-Truss-Optimization

2. Create the Conda environment: Use the provided environment.yml file to create the Conda environment. This will install all the necessary packages and dependencies.

   conda env create -f environment.yml

3. Activate the environment:

   conda activate cgp

Running the Experiment

To run the evolutionary algorithm, execute the main.py script:

python main.py

Acknowledgements

Environment modelling concepts originate from the RIED project (GitLab): https://gitlab.com/riedproject

• HAL-CGP

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