On initial populations of a genetic algorithm for continuous optimization problems

DOI10.1007/s10898-006-9056-6zbMath1156.90028OpenAlexW1973449924WikidataQ109314956 ScholiaQ109314956MaRDI QIDQ878224

Heikki Maaranen, Antti Penttinen, Kaisa M. Miettinen

Publication date: 26 April 2007

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-006-9056-6

zbMATH Keywords

Global optimizationEvolutionary algorithmsRandom number generationContinuous variablesInitial population

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Random number generation in numerical analysis (65C10)

Related Items (13)

Seeding the initial population with feasible solutions in metaheuristic optimization of steel trusses ⋮ Opposition-Based Computing ⋮ Hydrological cycle algorithm for continuous optimization problems ⋮ Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization ⋮ A hybrid genetic algorithm based on a two-level hypervolume contribution measure selection strategy for bi-objective flexible job shop problem ⋮ Initialization of metaheuristics: comprehensive review, critical analysis, and research directions ⋮ Meta-optimization of multi-objective population-based algorithms using multi-objective performance metrics ⋮ Optimization of a pumping ship trajectory to clean oil contamination in the open sea ⋮ A genetic algorithm for a global optimization problem arising in the detection of gravitational waves ⋮ Path planning on a cuboid using genetic algorithms ⋮ Global versus local search: the impact of population sizes on evolutionary algorithm performance ⋮ Genetic algorithms for outlier detection in multiple regression with different information criteria ⋮ A novel global optimization technique for high dimensional functions

Uses Software

• spatial
• Genocop
• Diehard
• GEATbx
• TOMS659

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