Amortized efficiency of list update and paging rules
Communications of the ACM
Competitive algorithms for server problems
Journal of Algorithms
Using genetic algorithms to solve NP-complete problems
Proceedings of the third international conference on Genetic algorithms
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An introduction to genetic algorithms
An introduction to genetic algorithms
Online computation and competitive analysis
Online computation and competitive analysis
Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Competitive k-server algorithms
SFCS '90 Proceedings of the 31st Annual Symposium on Foundations of Computer Science
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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This paper presents a novel application of Genetic Algorithms, as an empirical method in the analysis of algorithms. Online Algorithms are designed for the case in which the problem input does not arrive in its totality, as in Offline Algorithms, but arrives piece by piece, during the course of the computation. Generating worst-case instances for these algorithms, both for use as test cases and as lower-bound proofs, is often non-trivial. We study the use of Genetic Algorithms as a novel method for finding worst-case instances of online problems, including versions of the Taxicab Problem. These worst-case instances give us lower bounds on the non-competitiveness of the approximation algorithms used. In particular, our experimental results demonstrate that 6.93 is a lower bound on the competitive ratio of the hedging and optimal offline algorithms on the Hard Planar Taxicab Problem. This experimental result has theoretical implications for the study of the problem, i.e., further research to prove an upper bound of 7 may be warranted.