Real royal road functions for constant population size

Real Royal Road Functions for Constant Population Size

Evolutionary and genetic algorithms (EAs and GAs) are quite successful randomized function optimizers. This success is mainly based on the interaction of different operators like selection, mutation, and crossover. Since this interaction is still not well understood, one is interested in the analysis of the single operators. Jansen and Wegener (2001a) have described so-called real royal road fu...

Generalized Royal Road Functions and Their Epistasis

B.2.7.5: Fitness Landscapes: Royal Road Functions

An important goal of research on genetic algorithms (GAs) is to understand the class of problems for which GAs are most suited, and in particular, the class of problems on which they will outperform other search algorithms such as gradient methods. We have developed a class of tness landscapes|the \Royal Road" functions (Mitchell, Forrest, and Holland 1992; Forrest and Mitchell 1993)|that isola...

Mapping the Royal Road and other Hierarchical Functions

In this paper we present a technique for visualising hierarchical and symmetric, multi-modal fitness functions that have been investigated in the evolutionary computation literature. The focus of this technique is on landscapes in moderate-dimensional, binary spaces (i.e., fitness functions defined over [0,1](n), for n < or = 16). The visualisation approach involves an unfolding of the hyperspa...

Adaptive Evolutionary Algorithms on Unitation, Royal Road and Longpath Functions

Genetic algorithms (GAs) are powerful tools that allow engineers and scientists to find good solutions to hard computational problems using evolutionary principles. The classic genetic algorithm suffers from the configuration problem, the difficulty of choosing optimal parameter settings. Genetic algorithm literature is full of empirical tricks, techniques, and rules of thumb that enable GAs to...