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Harnessing memetic algorithms: a practical guide

Carlos Cotta

TOP - Transactions in Operations Research2025https://doi.org/10.1007/s11750-024-00694-8article
AJG 1ABDC B
Weight
0.48

What the paper says

Abstract The aim of this work is to provide a didactic approximation to memetic algorithms (MAs) and how to apply these techniques to an optimization problem. MAs are based on the synergistic combination of ideas from population-based metaheuristics and trajectory-based search/optimization techniques. Most commonly, MAs feature a population-based algorithm as the underlying search engine, endowing it with problem-specific components for exploring the search space, and in particular with local-search mechanisms. In this work, we describe the design of the different elements of the MA to fit the problem under consideration, and go on to perform a detailed case study on a constrained combinatorial optimization problem related to aircraft landing scheduling. An outline of some advanced topics and research directions is also provided.

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https://doi.org/https://doi.org/10.1007/s11750-024-00694-8

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@article{carlos2025,
  title        = {{Harnessing memetic algorithms: a practical guide}},
  author       = {Carlos Cotta},
  journal      = {TOP - Transactions in Operations Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s11750-024-00694-8},
}

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.