The Royal Road for Genetic Algorithms - Fitness Landscapes and GA Performance

Aug 20, 2026

Genetic algorithms (GAs) are widely used in artificial-life research, yet the mechanisms behind their performance and the theory for characterizing fitness landscapes remain limited. This paper proposes a feature-based strategy: define GA-relevant fitness landscape features, then experimentally test how different configurations of these features affect GA performance across multiple dimensions. The study introduces an initial set of feature classes, details the “Royal Road” function class, and presents early results on crossover and building blocks.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages11

Reading Time28 min

AccessFree to download

Introduction

GAs and Schema Processing

The Royal Road for Genetic Algorithms: Fitness Landscapes and GA Performance ¤ Melanie Mitchell AI Laboratory University of Michigan Ann Arbor, MI 48109 [melaniem@eecs.umich.edu](melaniem@eecs.umich.edu) Stephanie Forrest Dept. of Computer Science University of New Mexico Albuquerque, NM 87131 [forrest@unmvax.cs.unm.edu](forrest@unmvax.cs.unm.edu) John H. Holland Dept. of Psychology University of Michigan Ann Arbor, MI 48109 Abstract Genetic algorithms (GAs) play a major role in many arti¯cial-life systems, but there is often little detailed understanding of why the GA performs as it does, and little theoretical basis on which to characterize the types of ¯tness landscapes that lead to successful GA performance. In this paper we propose a strategy for addressing these issues. Our strategy consists of de¯ning a set of features of ¯tness landscapes that are particularly relevant to the GA, and experimentally studying how various con¯gurations of these features a®ect the GA's performance along a number of dimensions. In this paper we informally describe an initial set of proposed feature classes, describe in detail one such class (\Royal Road" functions), and present some initial experimental results concerning the role of crossover and \building blocks" on landscapes constructed from features of this class. 1 Introduction Evolutionary processes are central to our understanding of natural living systems, and will play an equally central role in attempts to create and study arti¯cial life. Genetic algorithms (GAs) [13 , 9] are an idealized computational model of Darwinian evolution based on the principles of genetic variation and natural selection. GAs have been employed in many arti¯cial-life systems as a means of evolving arti¯cial organisms, simulating ecologies, and modeling population evolution. In these and other applications, the GA's task n Toward a Practice of Autonomous Systems: Proceedings of the First European Conference on Arti¯cial Life Cambridge, MA: MIT Press, 1992. is to search a ¯tness landscape for high values (where ¯tness can be either explicitly or implicitly de¯ned), and GAs have been demonstrated to be e±cient and powerful search techniques for a range of such problems (e.g. , there are several examples in [19]) . However, the details of how the GA goes about searching a given landscape are not well understood. Consequently, there is little general understanding of what makes a problem hard or easy for a GA, and in particular, of the e®ects of various landscape features on the GA's performance. In this paper we propose some new methods for addressing these fundamental issues concerning GAs, and present some initial experimental results. Our strategy involves de¯ning a set of landscape features that are of particular relevance to GAs, constructing classes of landscapes containing these features in varying degrees, and studying in detail the e®ects of these features on the GA's behavior. The idea is that this strategy will lead to a better understanding of how the GA works, and a better ability to predict the GA's likely performance on a given landscape. Such longterm results would be of great importance to all researchers who use GAs in their models; we hope that they will also shed light on natural evolutionary systems. To date, several properties of ¯tness landscapes have been identi¯ed that can make the search for high-¯tness values easy or hard for the GA. These include deception, sampling error, and the number of local optima in the landscape (see Section 3 for details) . However, almost all the theoretical work on GA performance has been based on the assumption that deception is the leading cause of di±culty for the GA. This paper extends this work by (1) proposing several new relevant ¯tness landscape features, (2) studying one of these features in detail, and (3) demonstrating that there are \GA-easy" functions [27] which are not necessarily easy for the GA. 2 GAs and Schema P

FAQs

What main issue does the paper address about genetic algorithms?

It addresses the lack of detailed understanding and theoretical characterization of why GAs perform as they do on different fitness landscapes.

How does the paper propose to study GA performance?

By defining GA-relevant fitness landscape features, constructing landscape classes with varying feature configurations, and experimentally measuring how these features affect performance.

What is the focus of the detailed feature class presented in the paper?

The paper focuses on one proposed fitness landscape class described as “Royal Road” functions, including experiments on the roles of crossover and building blocks.

Aug 20, 2026

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