- author = "L. M. Deschaine and F. D. Francone",
- title = "Extending the Boundaries of Design Optimization by Integrating Fast Optimization Techniques with Machine Code Based, Linear Genetic Programming",
- booktitle = "Information Processing with Evolutionary Algorithms",
- year = "2005",
- editor = "Manuel Grana and Richard J. Duro and Alicia d'Anjou and Paul P. Wang",
- series = "Advanced Information and Knowledge Processing",
- chapter = "2",
- pages = "11--30",
- publisher = "Springer",
- keywords = "genetic algorithms, genetic programming",
- isbn13 = "978-1-85233-866-4",
-
URL = "
http://dx.doi.org/10.1007/1-84628-117-2_2",
-
DOI = "
doi:10.1007/1-84628-117-2_2",
-
abstract = "Summary and Conclusions
We are in the early stages of building a comprehensive, integrated optimisation and modelling system to handle complex industrial problems. We believe a combination of machine-code-based, LGP (for modelling) and ES CDSA (for optimization) together provides the best combination of available tools and algorithms for this task.
By conceiving of design optimisation projects as integrated modeling and optimisation problems from the outset, we anticipate that engineers and researchers will be able to extend the range of problems that are solvable, given today's technology.
The general approach of Deschaine and Francone is to reverse engineer a system with Linear Genetic Programming at the machine code level. This approach provides very fast and accurate models of the process that will be subject to optimisation. The optimisation process itself is performed using an Evolutionary Strategy with completely deterministic parameter self-adaptation. The authors have tested this approach in a variety of academic problems. They target industrial problems, characterised by low formalisation and high complexity. As a final illustration they deal with the design of an incinerator and the problem of subsurface unexploded ordnance detection.",
- notes = "cf FEA 2002 and JCIS 2002. Series editors Xindong Wu and Lakhmi Jain",
