The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Multiobjective evolutionary algorithm test suites
Abstract
Multiobjective Evolutionary Algorithms (MOEAs) currently have no generic benchmark test suites. This paper provides several Multiobjective Optimization Problems (MOPs) for use as part of a standardized MOEA test suite, and proposes a methodology whereby various MOEAs can be directly compared. Supporting these contributions is a detailed discussion of MOP landscape and general test suite issues, and presentation of a new theorem defining the structural limitations of an MOP's global optimum. This paper also discusses high-performance computer software deterministically computing an MOP's Pareto front at a given computational resolution. 1 Introduction Multiobjective Evolutionary Algorithms (MOEAs) are now a well-established field within Evolutionary Computation. They were "born" in 1985 when Schaffer [16] and Fourman [6] implemented the first MOEAs dealing with Multiobjective Optimization Problems (MOPs). Since then, over 140 published papers propose various MOEA implementations and a...
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Citations
Cites
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
Multiple Objective Decision Making — Methods and Applications
Hwang, C.-L., & Masud, A. S. M. (1979) · Lecture Notes in Economics and Mathematical Systems · doi:10.1007/978-3-642-45511-7 · Cited by 1,186
Metadata
- Authors
- van Veldhuizen, D. A., & Lamont, G. B.
- Venue
- Proceedings of the 1999 ACM symposium on Applied computing
- Publisher
- ACM
- Published
- 1999-02-28
- Pages
- 351-357
- OpenAlex
- W2043947065
- Cited by
- 447
- References
- 2
- FWCI
- 10.10
- Type
- conference paper
- Language
- en
- Open access
- closed
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