NDLI: Predicting Stochastic Search Algorithm Performance using Landscape State Machines

Content Provider	IEEE Xplore Digital Library
Author	Rowe, W. Corne, D. Knowles, J.
Copyright Year	2006
Description	Author affiliation: Univ. of Manchester, Manchester (Rowe, W.)
Abstract	A landscape state machine (LSM) is a Markov model describing the transition probabilities between the fitness 'levels' of an optimization problem, when a given neighbourhood (or mutation) operator is applied. Although most optimization problems cannot be modeled precisely by an LSM, an approximate LSM can always be constructed by sampling, and can be used, subsequently, in place of real fitness evaluations in order to model the performance of any search algorithm using the given neighbourhood operator. In this paper, we provide empirical evidence that (a) LSMs constructed by simulated annealing-based sampling of a problem landscape make accurate models in few evaluations; (b) LSMs can accurately rank the performance of diverse algorithms including EAs with/without niching and SA; (c) the LSM approach works on diverse problems from MAX-SAT to NKp; (d) convergence of the LSM can be used as a guide to stopping the sampling phase; and, (e) a single LSM constructed using a low mutation-rate sample is sufficient to accurately rank the performance of search algorithms run at multiples of this mutation rate.
Starting Page	2944
Ending Page	2951
File Size	599171
Page Count	8
File Format	PDF
ISBN	0780394879
DOI	10.1109/CEC.2006.1688679
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Prediction algorithms Mathematical model Sampling methods Statistical analysis Genetic mutations Algorithm design and analysis Testing Performance analysis Simulated annealing
Content Type	Text
Resource Type	Article

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