Journal article
Using assembly representations to enable evolutionary design of Lego structures
Artificial intelligence for engineering design, analysis and manufacturing, v 17(2)
May 2003
Abstract
This paper presents an approach to the automatic generation of
electromechanical engineering designs. We apply messy genetic algorithm
(GA) optimization techniques to the evolution of assemblies composed of
LegoTM structures. Each design is represented as a
labeled assembly graph and is evaluated based on a set of behavior
and structural equations. The initial populations are generated
at random, and design candidates for subsequent generations are
produced by user-specified selection techniques. Crossovers are
applied by using cut and splice operators at the random points of the
chromosomes; random mutations are applied to modify the graph with a
certain low probability. This cycle continues until a suitable design
is found. The research contributions in this work include the
development of a new GA encoding scheme for mechanical assemblies
(Legos), as well as the creation of selection criteria for this domain.
Our eventual goal is to introduce a simulation of electromechanical
devices into our evaluation functions. We believe that this research
creates a foundation for future work and it will apply GA techniques to
the evolution of more complex and realistic electromechanical
structures.
Metrics
Details
- Title
- Using assembly representations to enable evolutionary design of Lego structures
- Creators
- MAXIM Peysakhov - Drexel UniversityWILLIAM C. Regli - Drexel University
- Publication Details
- Artificial intelligence for engineering design, analysis and manufacturing, v 17(2)
- Publisher
- Cambridge University Press
- Number of pages
- 14
- Resource Type
- Journal article
- Language
- English
- Web of Science ID
- WOS:000186363600004
- Scopus ID
- 2-s2.0-0345356228
- Other Identifier
- 991019346801004721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Interdisciplinary Applications
- Engineering, Manufacturing
- Engineering, Multidisciplinary