Optimized crossover-based genetic algorithms for the maximum cardinality and maximum weight clique problems

DOI10.1023/A:1009646528813zbMath0917.68162OpenAlexW1605165687MaRDI QIDQ1273655

William Niehaus, Egon Balas

Publication date: 8 August 1999

Published in: Journal of Heuristics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/a:1009646528813

zbMATH Keywords

genetic algorithmoptimized crossover

Mathematics Subject Classification ID

Graph theory (including graph drawing) in computer science (68R10)

Related Items (14)

Subgraph extraction and metaheuristics for the maximum clique problem ⋮ Finding Maximum Clique in Stochastic Graphs Using Distributed Learning Automata ⋮ Genetic algorithms for a supply management problem: MIP-recombination vs greedy decoder ⋮ On complexity of optimal recombination for flowshop scheduling problems ⋮ A CPU-GPU local search heuristic for the maximum weight clique problem on massive graphs ⋮ Hybrid evolutionary algorithm with optimized operators for total weighted tardiness problem ⋮ Hybridizations of evolutionary algorithms with large neighborhood search ⋮ Optimised crossover genetic algorithm for capacitated vehicle routing problem ⋮ Combining metaheuristics with mathematical programming, constraint programming and machine learning ⋮ Combining metaheuristics with mathematical programming, constraint programming and machine learning ⋮ Variable neighborhood search for the maximum clique ⋮ A hybrid heuristic for the maximum clique problem ⋮ Distributed Load Identification for Uncertain Structure Based on LHS-GA and Improved L-Curve Method ⋮ Hitting times of local and global optima in genetic algorithms with very high selection pressure

Uses Software

• DIMACS

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