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This study introduces an evolutionary and developmental (evo-devo) framework to automate structural engineering design. The novel real-encoded NEAT algorithm effectively evolves diverse designs and outperforms existing methods.

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Area of Science:

  • Engineering
  • Computational Biology
  • Artificial Intelligence

Background:

  • Engineering design optimization often requires human expertise and struggles with generalizability.
  • Existing search techniques for design generation face limitations in parameter tuning and scalability.
  • Evolutionary and developmental (evo-devo) concepts offer a biologically inspired approach to automate complex design processes.

Purpose of the Study:

  • To introduce a novel framework for automating structural engineering design evolution using evo-devo principles.
  • To explore artificial gene regulatory networks (GRNs) implemented with neural networks and genetic programming.
  • To develop and evaluate a new neuroevolutionary method, real-encoded NEAT (RNEAT), for evolving diverse engineering designs.

Main Methods:

  • A framework inspired by evolutionary and developmental (evo-devo) biology was developed.
  • Artificial gene regulatory networks (GRNs) were emulated using neural networks and genetic programming.
  • A novel real value-encoded neuroevolutionary method, real-encoded NEAT (RNEAT), was introduced to evolve artificial GRNs.

Main Results:

  • The proposed framework successfully generated diverse structural designs for both 2-D and 3-D problems.
  • The real-encoded NEAT (RNEAT) algorithm demonstrated superior performance compared to established evolutionary techniques in over 50% of tested problems.
  • The study validated the concept of using evo-devo principles for automated engineering design evolution.

Conclusions:

  • The evo-devo-inspired framework provides an effective method for automating the evolution of structural engineering designs.
  • The RNEAT algorithm shows significant promise for generating diverse and optimized engineering solutions.
  • This research validates the potential of bio-inspired computational approaches in advancing engineering design optimization.