Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
A tutorial on learning with Bayesian networks
Learning in graphical models
Rule-extraction by backpropagation of polyhedra
Neural Networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Algorithms
Knowledge Acquisition in Civil Engineering
Knowledge Acquisition in Civil Engineering
On the discovery of process models from their instances
Decision Support Systems
Extract intelligible and concise fuzzy rules from neural networks
Fuzzy Sets and Systems - Fuzzy systems
A Coevolutionary Approach to Learning Sequential Decision Rules
Proceedings of the 6th International Conference on Genetic Algorithms
The Distributed Genetic Algorithm Revisited
Proceedings of the 6th International Conference on Genetic Algorithms
Distributed genetic algorithms for function optimization
Distributed genetic algorithms for function optimization
Some greedy learning algorithms for sparse regression and classification with mercer kernels
The Journal of Machine Learning Research
Case-based evolutionary design
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
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Previous applications of genetic programming (GP) have been restricted to searching for algebraic approximations mapping the design parameters (e.g., geometrical parameters) to a single design objective (e.g., weight). In addition, these algebraic expressions tend to be highly complex. By adding a simple extension to the GP technique, a powerful design data analysis tool is developed. This paper significantly extends the analysis capabilities of GP by searching for multiple simple models within a single population by splitting the population into multiple islands according to the design variables used by individual members. Where members from different islands “cooperate,” simple design models can be extracted from this cooperation. This relatively simple extension to GP is shown to have powerful implications to extracting design models that can be readily interpreted and exploited by human designers. The full analysis method, GP heuristics extraction method, is described and illustrated by means of a design case study.