Profiling the Distance Characteristics of Mutation Operators for Permutation-Based Genetic Algorithms

In this paper, we consider the permutation representation of genetic algorithms, and more generally, local search algorithms. We use a variety of permutation distance measures to profile the behavior of the most commonly used mutation operators for permutation-based genetic algorithms. Our operator profiles are also applicable to other local search algorithms, such as simulated annealing, as the most common permutation mutation operators are also commonly found as neighborhood operators for other metaheuristics in a search of the space of permutations. In addition to using several existing distance measures, we introduce two specific instances of the edit distance measure. Our aim is to offer the GA, and local search practitioner, guidance in the selection of mutation and neighborhood operators.

[1]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[2]  Kenneth Sörensen,et al.  Distance measures based on the edit distance for permutation-type representations , 2007, J. Heuristics.

[3]  Rafael Martí,et al.  Context-Independent Scatter and Tabu Search for Permutation Problems , 2005, INFORMS J. Comput..

[4]  James E. Smith,et al.  Self-Adaptation of Mutation Operator and Probability for Permutation Representations in Genetic Algorithms , 2010, Evolutionary Computation.

[5]  Terry Jones,et al.  Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms , 1995, ICGA.

[6]  S. Ronald,et al.  More distance functions for order-based encodings , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[7]  Vincent A. Cicirello,et al.  On the Design of an Adaptive Simulated Annealing Algorithm , 2007 .

[8]  Vincent A. Cicirello,et al.  Non-wrapping order crossover: an order preserving crossover operator that respects absolute position , 2006, GECCO.

[9]  D. J. Smith,et al.  A Study of Permutation Crossover Operators on the Traveling Salesman Problem , 1987, ICGA.

[10]  Dana Shapira,et al.  Edit distance with move operations , 2002, J. Discrete Algorithms.

[11]  Vicente Campos,et al.  Scatter Search vs. Genetic Algorithms , 2002 .

[12]  Andreas Fink,et al.  Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem , 2009, LION.

[13]  Christine L. Valenzuela,et al.  A Study of Permutation Operators for Minimum Span Frequency Assignment Using an Order Based Representation , 2001, J. Heuristics.

[14]  Michael J. Fischer,et al.  The String-to-String Correction Problem , 1974, JACM.