Correlated and uncorrelated fitness landscapes and how to tell the difference

By E. Weinberger. 1990

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Citations in the corpus

NAS-Bench-101: Towards Reproducible Neural Architecture Search (2019, neutral): “A popular measure of locality is the random-walk autocorrelation (RWA), deﬁned as the autocorrelation of the accuracies of points visited as we perform a long walk of random changes through the space (Weinberger, 1990; Stadler, 1996).”
Local Search is State of the Art for NAS Benchmarks (2020, methodology): “…to the experiment in NASBench-101 (Ying et al., 2019), we compute the random-walk autocorrelation (RWA), a popular measure of locality deﬁned as the autocorrelation of the accuracies of points visited during a walk of random single changes through the search space (Weinberger, 1990; Stadler, 1996).”
Optimal execution with non-linear transient market impact (2014, background): “Rugged landscapes have attracted attention in physics Weinberger (1991), evolutionary biology (Weinberger 1990) and computer science.”
Global Optimization Algorithms -- Theory and Application (2009, methodology): “As a measure for the ruggedness of a fitness landscape, the autocorrelation function as well as the correlation length of random walks can be used [90].”
Algorithm runtime prediction: Methods & evaluation (2012, background): “Prominent examples include fitness distance correlation [66] and autocorrelation length (ACL) [113].”
The evolvability of artificial neural net-works for robot control (2002, methodology): “4 we described ruggedness in the tunably rugged NK landscapes using the correlation length τ (Weinberger, 1990).”
Local Search is State of the Art for Neural Architecture Search Benchmarks (2020, methodology): “defined as the autocorrelation of the accuracies of points visited during a random walk on the neighborhood graph [34, 32], and was used to measure locality in NASBench-101 in prior work [39].”
ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution (2024, unknown): “Based on the autocorrelation function, correlation length is defined below (Weinberger, 1990).”
Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, background): “Hence, many different metrics of EA problem hardness have been explored (Davidor, 1990; Hordijk, 1995; Jones and Forrest, 1995; Kauffman, 1993; Manderick et al., 1991; Weinberger, 1990).”
Iterated local search for the quadratic assignment problem (2006, background): “Central to the search space analysis of combinatorial optimization problems is the notion of search landscape [40,50].”
Selectionism and Learning in Projects with Complexity and Unforeseeable Uncertainty (2004, background): “…as follows: “The ‘distance’ between two points on the landscape is the smallest number of generators (“steps”) needed to move from one point to the other” (Weinberger 1990, p. 328), where generators or steps are the most elementary transformations that, used in a succession, can transform one…”