Evolving artificial intelligence
Evolving artificial intelligence
A compiling genetic programming system that directly manipulates the machine code
Advances in genetic programming
The nature of statistical learning theory
The nature of statistical learning theory
Explicitly defined introns and destructive crossover in genetic programming
Advances in genetic programming
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Advances in genetic programming
Evolution and Optimum Seeking: The Sixth Generation
Evolution and Optimum Seeking: The Sixth Generation
Genetic Programming III: Darwinian Invention & Problem Solving
Genetic Programming III: Darwinian Invention & Problem Solving
Contemporary Evolution Strategies
Proceedings of the Third European Conference on Advances in Artificial Life
Evolving Turing-Complete Programs for a Register Machine with Self-modifying Code
Proceedings of the 6th International Conference on Genetic Algorithms
Complexity Compression and Evolution
Proceedings of the 6th International Conference on Genetic Algorithms
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Dynamic population variation in genetic programming
Information Sciences: an International Journal
A machine code-based genetic programming for suspended sediment concentration estimation
Advances in Engineering Software
Engineering Applications of Artificial Intelligence
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Optimized models of complex physical systems are difficult to create and time consuming to optimize. The physical and business processes are often not well understood and are therefore difficult to model. The models of often too complex to be well optimized with available computational resources. Too often approximate, less than optimal models result. This work presents an approach to this problem that blends three well-tested components. First: We apply Linear Genetic Programming (LGP) to those portions of the system that are not well understood--for example, modeling data sets, such the control settings for industrial or chemical processes, geotechnical property prediction or UXO detection. LGP builds models inductively from known data about the physical system. The LGP approach we highlight is extremely fast and builds rapid to execute, high-precision models of a wide range of physical systems. Yet it requires few parameter adjustments and is very robust against overfitting. Second: We simulate those portions of the system--for example, the cost model for the processes--these are well understood with human built models. Finally: We optimize the resulting meta-model using Evolution Strategies (ES). ES is a fast, general-purpose optimizer that requires little pre-existing domain knowledge. We have developed this approach over a several years period and present results and examples that highlight where this approach can greally improve the development and optimization of complex physical systems.