A general-purpose tunable landscape generator

Gallagher, Marcus and Yuan, Bo (2006) A general-purpose tunable landscape generator. IEEE Transactions On Evolutionary Computation, 10 5: 590-603. doi:10.1109/TEVC.2005.863628

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Author Gallagher, Marcus
Yuan, Bo
Title A general-purpose tunable landscape generator
Journal name IEEE Transactions On Evolutionary Computation   Check publisher's open access policy
ISSN 1089-778X
Publication date 2006
Sub-type Article (original research)
DOI 10.1109/TEVC.2005.863628
Volume 10
Issue 5
Start page 590
End page 603
Total pages 14
Editor D. B. Fogel
Place of publication Piscataway
Publisher IEEE-Inst Electrical Electronics Engineers Inc
Collection year 2006
Language eng
Subject C1
280212 Neural Networks, Genetic Alogrithms and Fuzzy Logic
700199 Computer software and services not elsewhere classified
Abstract The research literature on metalieuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metalieuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.
Keyword Continuous Optimization
Empirical Algorithm Analysis
Estimation Of Distribution Algorithm
Test-problem Generator
Computer Science, Artificial Intelligence
Computer Science, Theory & Methods
Evolutionary Algorithms
Genetic Algorithms
Optimization
Operators
Search
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

 
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