This paper describes the application of an arti cial immune system, AIS, model to a scheduling application, in which sudden changes in the scheduling environment require the rap...
From the user’s point of view, setting the parameters of a genetic algorithm (GA) is far from a trivial task. Moreover, the user is typically not interested in population sizes,...
In this paper, we discuss the adaptability of Coevolutionary Genetic Algorithms on dynamic environments. Our CGA consists of two populations: solution-level one and schema-level o...
The Terrain-Based Genetic Algorithm (TBGA) is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, various combinations of parameter values appear...
V. Scott Gordon, Rebecca Pirie, Adam Wachter, Scot...
X-ray spectroscopic analysis is a powerful tool for plasma diagnostics. We use genetic algorithms to automatically analyze experimental X-ray line spectra and discuss a particular...
Igor E. Golovkin, Roberto C. Mancini, Sushil J. Lo...
In this paper, we apply an evolutionary algorithm to learning behavior on a novel, interesting task to explore the general issue of learning e ective behaviors in a complex enviro...
Top-down or analytical provers based on the connection tableau calculus are rather powerful, yet have notable shortcomings regarding redundancy control. A well-known and successfu...
In genetic programming a general consensus is that the population should be as large as practically possible or sensible. In this paper we examine a batch of problems of combinato...
In recent years, the genetic programming crossover operator has been criticized on both theoretical and empirical grounds. This paper introduces a new crossover operator for linea...
Frank D. Francone, Markus Conrads, Wolfgang Banzha...