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EUROGP
2008
Springer
135views Optimization» more  EUROGP 2008»
9 years 1 months ago
Using Genetic Programming for Turing Machine Induction
Abstract. Turing machines are playing an increasingly significant role in Computer Science domains such as bioinformatics. Instead of directly formulating a solution to a problem, ...
Amashini Naidoo, Nelishia Pillay
EUROGP
2008
Springer
137views Optimization» more  EUROGP 2008»
9 years 1 months ago
A Comparison of Cartesian Genetic Programming and Linear Genetic Programming
Two prominent genetic programming approaches are the graph-based Cartesian Genetic Programming (CGP) and Linear Genetic Programming (LGP). Recently, a formal algorithm for construc...
Garnett Carl Wilson, Wolfgang Banzhaf
IEEEARES
2010
IEEE
9 years 2 months ago
Improving Network Intrusion Detection by Means of Domain-Aware Genetic Programming
—One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore t...
Jorge Blasco Alís, Agustín Orfila, A...
GECCO
2010
Springer
249views Optimization» more  GECCO 2010»
9 years 2 months ago
Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach
Developing dispatching rules for manufacturing systems is a tedious process, which is time- and cost-consuming. Since there is no good general rule for different scenarios and ob...
Torsten Hildebrandt, Jens Heger, Bernd Scholz-Reit...
ACMICEC
2008
ACM
270views ECommerce» more  ACMICEC 2008»
9 years 2 months ago
Adaptive strategies for predicting bidding prices in supply chain management
Supply Chain Management (SCM) involves a number of interrelated activities from negotiating with suppliers to competing for customer orders and scheduling the manufacturing proces...
Yevgeniya Kovalchuk, Maria Fasli
GECCO
2010
Springer
169views Optimization» more  GECCO 2010»
9 years 3 months ago
Robust symbolic regression with affine arithmetic
We use affine arithmetic to improve both the performance and the robustness of genetic programming for symbolic regression. During evolution, we use affine arithmetic to analyze e...
Cassio Pennachin, Moshe Looks, João A. de V...
GECCO
2010
Springer
158views Optimization» more  GECCO 2010»
9 years 3 months ago
Efficiently evolving programs through the search for novelty
A significant challenge in genetic programming is premature convergence to local optima, which often prevents evolution from solving problems. This paper introduces to genetic pro...
Joel Lehman, Kenneth O. Stanley
EUROGP
2010
Springer
160views Optimization» more  EUROGP 2010»
9 years 3 months ago
Fine-Grained Timing Using Genetic Programming
In previous work, we have demonstrated that it is possible to use Genetic Programming to minimise the resource consumption of software, such as its power consumption or execution t...
David Robert White, Juan E. Tapiador, Julio C&eacu...
EPS
1995
Springer
9 years 3 months ago
Evolving the Architecture of a Multi-part Program in Genetic Programming Using Architecture-Altering Operations
: This paper describes six new architecture-altering operations that provide a way to dynamically determine the architecture of a multipart program during a run of genetic programm...
John R. Koza
AIIA
1995
Springer
9 years 3 months ago
Learning Programs in Different Paradigms using Genetic Programming
Genetic Programming (GP) is a method of automatically inducing programs by representing them as parse trees. In theory, programs in any computer languages can be translated to par...
Man Leung Wong, Kwong-Sak Leung
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