Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 1,278

The DOI matches its Crossref record.

The Pareto archived evolution strategy: a new baseline algorithm for Pareto multiobjective optimisation

Knowles, J., Corne, D.·2003·Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
DOI 10.1109/cec.1999.781913
Open this paper in CoChat — free account

Abstract

Most popular evolutionary algorithms for multiobjective optimisation maintain a population of solutions from which individuals are selected for reproduction. In this paper, we introduce a simpler evolution scheme for multiobjective problems, called the Pareto archived evolution strategy (PAES). We argue that PAES may represent the simplest possible non-trivial algorithm capable of generating diverse solutions in the Pareto optimal set. The algorithm is identified as being a (1+1) evolution strategy, using local search from a population of one but using a reference archive of previously found solutions in order to identify the approximate dominance ranking of the current and candidate solution vectors. PAES is intended as a good baseline approach, against which more involved methods may be compared, and may also serve well in some real-world applications when local search seems superior to or competitive with population-based methods. The performance of the new algorithm is compared with that of a MOEA based on the niched Pareto GA on a real world application from the telecommunications field. In addition, we include results from experiments carried out on a suite of four test functions, to demonstrate the algorithm's general capability.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/cec.1999.781913

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 14

  1. 10.1162/evco.1994.2.3.221 · DOI · Paper page
  2. 10.1287/mnsc.17.11.712 · DOI · Paper page
  3. parks, 1998, Selective breeding in a multiobjective genetic algorithm, Parallel Problem Solving from Nature 5th International Conference Proceedings
  4. schaffer, 1985, Multiple objective optimization with vector evaluated genetic algorithm, Proceedings of the 1st International Conference on Genetic Algorithms, 93
  5. 10.1162/evco.1995.3.1.1 · DOI · Paper page
  6. bentley, 1997, Finding acceptable solutions in the pareto-optimal range using multiobjective genetic algorithms, The 2nd On-line World Conference on Soft Computing in Engineering Design and Manufacturing (SC2)
  7. ba?ck, 1996, Thomas Ba?ck Evolutionary Algorithms in Theory and Practice, 10.1093/oso/9780195099713.001.0001 · DOI
  8. mann, 1996, A comparison of heuristics for telecommunications traffic routing, Modern Heuristic Search Methods
  9. hansen, 1997, Generating a diversity of good solutions to a practical combinatorial problem using vectorized simulated annealing, Control and Cybernetics
  10. hansen, 1996, Tabu search for multiobjective optimization: MOTS, MCDM'97
  11. goldberg, 1989, Genetic Algorithms in Search Optimization and Machine Learning
  12. fonseca, 1995, On the performance assessment and comparison of stochastic multiobjective optimisers. Parallel problem solving from nature, Parallel Problem Solving from Nature - PPSN IV, 1141, 584, 10.1007/3-540-61723-X_1022 · DOI
  13. horn, 1994, Multiobjective optimisation using the niched pareto genetic algorithm, IlliGAL Report 93005 Illinois Genetic Algorithms Lab
  14. 10.1109/ICEC.1994.350037 · DOI

Cited by

Metadata

Authors
Knowles, J., & Corne, D.
Venue
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406)
Publisher
IEEE
Published
2003-01-20
Pages
98-105
DOI
10.1109/cec.1999.781913
OpenAlex
W1957430778
Cited by
1,278
References
0
FWCI
31.66
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.