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Evolutionary Lamarckism
Principle where improvements acquired during local search are directly transmitted to the next generation.
Baldwinian
Approach where learning improves fitness but does not modify the genotype, only influencing selection probability.
Global Hybridization
Strategy applying local search to all individuals in the population at each generation.
Local Hybridization
Selective application of local search only to certain individuals chosen according to specific criteria.
Hybridization Scheme
Strategy defining when and how to apply local search in the evolutionary process.
Adaptive Memetic
Algorithm dynamically adjusting the frequency and intensity of local search according to convergence state.
Multi-restart Memetic
Strategy restarting the algorithm with new populations after convergence to explore various attraction basins.
Individual Learning
Process of improving a specific solution via local search before its integration into the population.
Memetic Archipelago
Parallel architecture where several sub-populations evolve independently with periodic exchanges of improved individuals.
Self-organized Memeticism
System where the application of local search emerges spontaneously without predefined centralized control.
Memetic Operator
Specific transformation combining genetic operators and local search to create high-quality solutions.
Hybrid Computing Time
Computational budget allocated between global search and local search to optimize performance.