Structural shape optimization — a survey
Computer Methods in Applied Mechanics and Engineering
Classifier systems and genetic algorithms
Artificial Intelligence
General shape optimization capability
Finite Elements in Analysis and Design - NASTRAN Issue
Zeroth-order shape optimization utilizing a learning classifier system
Zeroth-order shape optimization utilizing a learning classifier system
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
Empirical studies of default hierarchies and sequences of rules in learning classifier systems
Zcs: A zeroth level classifier system
Evolutionary Computation
A Learning Classifier Systems Bibliography
Learning Classifier Systems, From Foundations to Applications
A Roadmap to the Last Decade of Learning Classifier System Research
Learning Classifier Systems, From Foundations to Applications
A Bigger Learning Classifier Systems Bibliography
IWLCS '00 Revised Papers from the Third International Workshop on Advances in Learning Classifier Systems
Recent trends in learning classifier systems research
Advances in evolutionary computing
| Hi-index | 0.01 |
A methodology to perform generalized zeroth-order two- and three-dimensional shape optimization utilizing a learning classifier system is presented and applied. Specifically, the methodology has the objective of determining the optimal boundary to minimize mass while satisfying constraints on stress and geometry. Even with the enormous advances in shape optimization no method has proven to be satisfactory across the broad spectrum of optimization problems facing the modern engineer. The shape optimization via hypothesizing inductive classifier system complex (SPHINcsX) instantiates the methodology in a software package overcoming many of the limitations of today's conventional shape optimization techniques. From environmental input, and a population of initially randomly generated rules, SPHINcsX is expected to learn to make the appropriate component shape modifications to reach a minimum mass design while satisfying all stress constraints. SPHINcsX not only learns from a clean slate, but confronts the additional challenge of learning without sensitivity information that most other shape optimization algorithms deem essential. SPHINcsX has proven adept at solving both two- and three-dimensional shape optimization problems.