![]() Hernando L, Mendiburu A, Lozano JA (2016) A tunable generator of instances of permutation-based combinatorial optimization problems. AAAI Press, pp 46–51įagin R, Kumar R, Sivakumar D (2003) Comparing top k lists. Ĭicirello VA, Cernera R (2013) Profiling the distance characteristics of mutation operators for permutation-based genetic algorithms. Ĭicirello VA (2022) Cycle mutation: Evolving permutations via cycle induction. Springer Nature, New York, pp 81–97 Ĭicirello VA (2020) Chips-n-Salsa: A java library of customizable, hybridizable, iterative, parallel, stochastic, and self-adaptive local search algorithms. In: Proc 11th int conf on bio-inspired information and communication technologies. ![]() Ĭicirello VA (2019) Classification of permutation distance metrics for fitness landscape analysis. Ĭicirello VA (2018) JavaPermutationTools: A java library of permutation distance metrics. pp 28–35 Ĭicirello VA (2016) The permutation in a haystack problem and the calculus of search landscapes. In: Proc int conf on bioinspired information and communications technologies. pp 75–83Ĭicirello VA (2014) On the effects of window-limits on the distance profiles of permutation neighborhood operators. INFORMS J Comput 17(1):111–122Ĭaprara A (1997) Sorting by reversals is difficult. pp 14–21Ĭampos V, Laguna M, Marti R (2005) Context-independent scatter and tabu search for permutation problems. pp 121–135īaker J (1987) Reducing bias and inefficiency in the selection algorithm. All of the code necessary to recreate our analysis and experimental results are also available as open source.Īckley DH (1985) A connectionist algorithm for genetic search. Our implementations of the permutation metrics, permutation mutation operators, and associated evolutionary algorithm, are available in a pair of open source Java libraries. From this, we present a classification of a variety of mutation operators as a counterpart to that of the metrics. Using optimization problems of each class, we also demonstrate how the classification scheme can subsequently inform the choice of mutation operator within an evolutionary algorithm. We see that the classification can assist in identifying appropriate metrics based on optimization problem feature for use in fitness landscape analysis. Additionally, the formal analysis identifies subtypes within these problem categories. The result of this analysis aligns with existing classifications of permutation problem types produced through less formal means, including the A-permutation, R-permutation, and P-permutation types, which classifies problems by whether absolute position of permutation elements, relative positions of elements, or general precedence of pairs of elements, is the dominant influence over solution fitness. We begin with a survey of the available distance metrics for permutations, and then use principal component analysis to classify these metrics. ![]() ![]() Many of the computational and analytical tools for fitness landscape analysis, such as fitness distance correlation, require identifying a distance metric for measuring the similarity of different solutions to the problem. In this paper, we explore the theory and expand upon the practice of fitness landscape analysis for optimization problems over the space of permutations. ![]()
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