The vehicle routing problem
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Memetic Algorithms and the Fitness Landscape of the Graph Bi-Partitioning Problem
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Analysis of Fitness Landscape Properties for Evolutionary Antenna Design
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Smoothness, ruggedness and neutrality of fitness landscapes: from theory to application
Advances in evolutionary computing
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
Vehicle Routing Problem with Time Windows, Part II: Metaheuristics
Transportation Science
A search space analysis for the waste collection vehicle routing problem with time windows
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Fifty Years of Vehicle Routing
Transportation Science
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Multidimensional Knapsack Problem: A Fitness Landscape Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
In this paper we examine the predictability of genetic algorithm (GA) performance using information-theoretic fitness landscape measures. The outcome of a GA is largely based on the choice of search operator, problem representation and tunable parameters (crossover and mutation rates, etc). In particular, given a problem representation the choice of search operator will determine, along with the fitness function, the structure of the landscape that the GA will search upon. Statistical and information theoretic measures have been proposed that aim to quantify properties (ruggedness, smoothness, etc) of this landscape. In this paper we concentrate on the utility of information theoretic measures to predict algorithm output for various instances of the capacitated and time-windowed vehicle routing problem. Using a clustering-based approach we identify similar landscape structures within these problems and propose to compare GA results to these clusters using performance profiles. These results highlight the potential for predicting GA performance, and providing insight self-configurable search operator design.