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Uncovering structural bias in population-based optimization algorithms: A theoretical and simulation-based analysis of the Generalized Signature Test

  • Published:2024-04 Issue: Volume:240 Page:122332
  • ISSN:0957-4174
  • Container-title:Expert Systems with Applications
  • language:en
  • Short-container-title:Expert Systems with Applications

Author:

Rajwar KanchanORCID,Deep KusumORCID

Funder

Council of Scientific and Industrial Research, India

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference48 articles.

1. Using selection to improve particle swarm optimization;Angeline,1998

2. Quantum random number generator;Australian National University,2023

3. Quantum random number generator: Random image (B&W);Australian National University,2023

4. Analysis of a random forests model;Biau;Journal of Machine Learning Research,2012

5. The job shop scheduling problem: Conventional and new solution techniques;Błażewicz;European Journal of Operational Research,1996

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1. Investigating Structural Bias in Real-Coded Genetic Algorithms;Proceedings of the Genetic and Evolutionary Computation Conference Companion;2024-07-14

2. Discrete Marine Predators Algorithm for Symmetric Travelling Salesman Problem;Evolutionary Intelligence;2024-07-02

3. Performance of Beta Mutation on CEC 2017 and CEC 2022 Benchmarks;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

4. Q-Learning-Driven Framework for High-Dimensional Optimization Problems;2024 IEEE Congress on Evolutionary Computation (CEC);2024-06-30

5. Analysis of Marine Predators Algorithm using BIAS toolbox and Generalized Signature Test;Alexandria Engineering Journal;2024-05
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