2001/04/06 by Theodore C. Belding, Belding, Theodore C.
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Adaptation and Self-Organizing Systems (nlin.AO) #Evolution and Genetic Dynamics #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #FOS: Physical sciences #I.2.m #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE) #cs.NE #nlin.AO
paper · pdf · doi:10.48550/arxiv.cs/0104011
8 pages; to appear in GECCO 2001
arxiv created 2001/04/06 · openalex publication_date 2001/04/06 · arxiv updated 2009/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
It is still unclear how an evolutionary algorithm (EA) searches a fitness landscape, and on what fitness landscapes a particular EA will do well. The validity of the building-block hypothesis, a major tenet of traditional genetic algorithm theory, remains controversial despite its continued use to justify claims about EAs. This paper outlines a research program to begin to answer some of these open questions, by extending the work done in the royal road project. The short-term goal is to find a simple class of functions which the simple genetic algorithm optimizes better than other optimization methods, such as hillclimbers. A dialectical heuristic for searching for such a class is introduced. As an example of using the heuristic, the simple genetic algorithm is compared with a set of hillclimbers on a simple subset of the hyperplane-defined functions, the pothole functions.