GGA: a modified genetic algorithm with gradient-based local search for solving constrained optimization problems

DOI10.1016/j.ins.2020.08.040zbMath1475.68477OpenAlexW3063913079MaRDI QIDQ2056278

Francesco Palmieri, Gianni D'Angelo

Publication date: 2 December 2021

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2020.08.040

zbMATH Keywords

heuristicsconstrained optimizationhybrid genetic algorithmsevolutionary algorithmsgradient descent

Mathematics Subject Classification ID

Evolutionary algorithms, genetic algorithms (computational aspects) (68W50) Nonlinear programming (90C30) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

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Uses Software

• Genocop
• CEC 05

Cites Work

• Unnamed Item
• A literature survey of benchmark functions for global optimisation problems
• The parallel genetic algorithm as function optimizer
• Hybrid genetic algorithm for optimization problems with permutation property
• A genetic algorithm based augmented Lagrangian method for constrained optimization
• A hybrid genetic algorithm with the Baldwin effect