Evolutionary Algorithms, Fitness Landscapes and Search

By Tim Jones. 1995

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Citations in the corpus

NAS-Bench-101: Towards Reproducible Neural Architecture Search (2019, methodology): “To measure locality within this neighborhood, we used the fitness-distance correlation metric (FDC, Jones et al. (1995)).”
Complex Adaptations and the Evolution of Evolvability (2005, background): “Another approach (Jones and Forrest, 1995) measures the correlation between tness or performance and the distance from the optimum in the search space as a predictor of how well adaptation proceeds.”
Automated Algorithm Selection: Survey and Perspectives (2018, background): “…f : X → Y , which maps vectors of variables, x = ( x 1 , . . . , x d ) , from a d -dimensional decision space X ⊆ R d (whose values may be subject to additional constraints) to p -dimensional objective or ﬁtness values y = ( y 1 , . . . , y p ) := f ( x ) ∈ Y ⊆ R p (Jones, 1995; Stadler, 2002).”
Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions (2014, background): “Jones (1995) and Stadler (2002) provide a formal definition of the fitness landscape, where the landscape is defined as a directed graph, where the nodes correspond to solutions, and the edges (connections) to search operator manoeuvres.”
A Theoretical and Empirical Study of Search-Based Testing: Local, Global, and Hybrid Search (2010, background): “[31], classically used to identify whether crossover is actively”
Bio-inspired computation: Where we stand and what's next (2019, background): “Other interesting properties that can be inferred from landscape analysis range from the runtime performance estimation to the evolvability of a population of individuals induced by the landscape, the neutrality of the landscape, the discovery of optimal parametric settings for heuristic operators o”
Advances in Computational Intelligence (2003, unknown): “Another insight into the behavior of the evolutionary algorithm is provided by means of fitness landscape analysis, which allows the designer to get partial information about the problem [85].”
A Gentle Introduction to Memetic Algorithms (2003, background): “The combination of a certain problem instance and the three entities defined above induces a so-called fitness landscape [106].”
Classification of adaptive memetic algorithms: a comparative study (2006, unknown): “Further, statistical measure may then be used to characterize fitness landscapes or neighborhood structures [41], [42] and the success of the memes on them.”
A tutorial for competent memetic algorithms: model, taxonomy, and design issues (2005, background): “This fact was recognised by Jones’ “One Operator, One Landscape” axiom [104].”
Evolutionary computation: a unified approach (2018, background): “These ideas are explored in considerable detail in Jones (1995).”