Book Chapter10.1007/978-3-319-91446-6_25
Genetic-Algorithm-Driven MIMO Multi-user Detector for Wireless Communications
Mohammed J. Khafaji,Maciej Krasicki +1 more
- 02 Jul 2018
- pp 258-269
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TL;DR: In the paper, evolutionary optimization strategy, represented by the genetic algorithm (GA) is considered as a multiuser detection method for a multiple-input multiple-output (MIMO) wireless system and Zero-Forcing (ZF) detection is proposed as an initial processing phase.
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Abstract: In the paper, evolutionary optimization strategy, represented by the genetic algorithm (GA) is considered as a multiuser detection (MUD) method for a multiple-input multiple-output (MIMO) wireless system. With the aim to boost lacking GA convergence, Zero-Forcing (ZF) detection is proposed as an initial processing phase. Additionally, a multi-stage GA routine is considered as a method to make the search for data estimates more effective.
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Citations
Successive-Interference-Cancellation-Inspired Multi-user MIMO Detector Driven by Genetic Algorithm
Mohammed J. Khafaji,Maciej Krasicki +1 more
- 29 Jun 2020
TL;DR: This paper re-visits the solution to the MUD problem, based on the use of Genetic Algorithm, and introduces a re-designed method to generate the initial GA population, which improves the performance at no extra computational cost in comparison with the previous proposal.
1
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An Introduction to Genetic Algorithms
Melanie Mitchell
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TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
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Practical Genetic Algorithms
Randy L. Haupt,Sue Ellen Haupt +1 more
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TL;DR: Introduction to Optimization The Binary genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.
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On the practical genetic algorithms
Chang Wook Ahn,Sanghoun Oh,R. S. Ramakrishna +2 more
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TL;DR: Practical design-guidelines for developing efficient genetic algorithms to successfully solve real-world problems are offered and a practical population-sizing model is presented and verified.
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Introduction to Genetic Algorithms for Scientists and Engineers
David A. Coley
- 29 Jan 1999
TL;DR: Improving the algorithm foundations advanced operators writing a genetic algorithm applications of genetic algorithms and showing the benefits of incorporating reinforcement learning into genetic algorithms.
1.1K
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