Work

Tunably Rugged Landscapes with Known Maximum and Minimum

Year: 2014

Type: preprint

Abstract: We propose NM landscapes as a new class of tunably rugged benchmark problems. NM landscapes are well-defined on alphabets of any arity, including both discrete and real-valued alphabets, include epistasis in a natural and transparent manner, are proven to have known value and location of the global maximum and, with some additional constraints, are proven to also have a known global minimum. Empirical studies are used to illustrate that, when coefficients are selected from a recommended distribution, the ruggedness of NM landscapes is smoothly tunable and correlates with several measures of search difficulty. We discuss why these properties make NM landscapes preferable to both NK landscapes and Walsh polynomials as benchmark landscape models with tunable epistasis.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1409.1143

Authors: Manukyan, Narine, Eppstein, Margaret J., Buzas, Jeffrey S.

Institution: University of Vermont

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: North Carolina State University, Michigan State University, University of Vermont, National Institutes of Health, College of Engineering, Michigan State University

OpenAlex ID: W2952931653

Cites: 19

Cited by: 2

Related to: 20