Abstract. Genetic Programming often uses excessive computational resources because the population size and the maximum number of generations per run are not optimized. We have deve...
For problems where the evaluation of an individual is the dominant factor in the total computation time of the evolutionary process, minimizing the number of evaluations becomes cr...
A reduced model technique based on a reduced number of numerical simulations at a subset of operating conditions for a perfectly stirred reactor is developed in order to increase t...
Lionel Elliott, Derek B. Ingham, Adrian G. Kyne, N...
Two efficient clustering-based genetic algorithms are developed for the optimisation of reaction rate parameters in chemical kinetic modelling. The genetic algorithms employed are ...
Lionel Elliott, Derek B. Ingham, Adrian G. Kyne, N...
Abstract. In practical applications evaluating a fitness function is frequently subject to noise, i. e., the “true fitness” is disturbed by some random variations. Evolutiona...
Spiking neural networks are computationally more powerful than conventional artificial neural networks. Although this fact should make them especially desirable for use in evoluti...
Rich Drewes, James B. Maciokas, Sushil J. Louis, P...
In this paper, we present simple and genetic forms of an evolutionary paradigm known as a society of hill-climbers (SoHC). We compare these simple and genetic SoHCs on a test suite...
Gerry V. Dozier, Hurley Cunningham, Winard Britt, ...
Artificial Immune Systems (AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems (IDSs). This paper describes how the des...
Gerry V. Dozier, Douglas Brown, John Hurley, Kryst...
Abstract. Designing storage area networks is an NP-hard problem. Previous work has focused on traditional algorithmic techniques to automatically determine fabric requirements, net...
Elizabeth Dicke, Andrew Byde, Paul J. Layzell, Dav...