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kategorie
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podkategorie
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pojęcia
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pojęcia

Evolutionary Lamarckism

Principle where improvements acquired during local search are directly transmitted to the next generation.

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Baldwinian

Approach where learning improves fitness but does not modify the genotype, only influencing selection probability.

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Global Hybridization

Strategy applying local search to all individuals in the population at each generation.

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Local Hybridization

Selective application of local search only to certain individuals chosen according to specific criteria.

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Hybridization Scheme

Strategy defining when and how to apply local search in the evolutionary process.

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Adaptive Memetic

Algorithm dynamically adjusting the frequency and intensity of local search according to convergence state.

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Multi-restart Memetic

Strategy restarting the algorithm with new populations after convergence to explore various attraction basins.

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Individual Learning

Process of improving a specific solution via local search before its integration into the population.

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Memetic Archipelago

Parallel architecture where several sub-populations evolve independently with periodic exchanges of improved individuals.

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Self-organized Memeticism

System where the application of local search emerges spontaneously without predefined centralized control.

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Memetic Operator

Specific transformation combining genetic operators and local search to create high-quality solutions.

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Hybrid Computing Time

Computational budget allocated between global search and local search to optimize performance.

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