HIGHER EPISTASIS IN GENETIC ALGORITHMS

Epistasis in Genetic Algorithms: An Experimental Design Perspective

In an earlier paper we examined the relationship between genetic algorithms (GAs) and traditional methods of experimental design. This was motivated by an investigation into the problems caused by epistasis in the implementation and application of GAs to optimization problems. We showed how this viewpoint enables us to gain further insights into the determination of epistatic effects , and into...

Epistasis Variance: Suitability of a Representation to Genetic Algorithms

The most problematic aspect in the application of a genetic algorithm (GA) is the coding of the problem. In superficial applications, choosing a representation may appear simple. Yet it is really an art because the theory provides only partial directives and is not always fully applicable. Different representations incorporate varying degrees of nonlinearity among the representation elements . ...

Genetic Algorithms and Higher Order Perceptrons

Constructive induction, which is defined to be the process of constructing new and useful features from existing ones, has been extensively studied in the literature. Since the number of possible high order featuresfor any given leaming problem is exponential in the number of input attributes (where the order of a feature is defined to be the number of attributes of which it is composed), the m...

Identifying the importance of higher order epistasis

Higher order epistasis and fitness peaks

We show that higher order epistasis has a substantial impact on evolutionary dynamics by analyzing peaks in the fitness landscapes. There are 193,270,310 fitness graphs, or cube orientations, for 4-locus systems, distributed on 511, 863 isomorphism classes. We identify all fitness graphs with 6 or more peaks. 81 percent of them imply 4-way epistasis, whereas 9 percent of all 4-locus fitness gra...