Research on hybrid genetic algorithm based on double structure coding

Fan Wang; Renjun Yan; Yu Qiu

doi:10.1117/12.3011779

7 December 2023 Research on hybrid genetic algorithm based on double structure coding

Fan Wang, Renjun Yan, Yu Qiu

Proceedings Volume 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023); 129410O (2023) https://doi.org/10.1117/12.3011779
Event: Third International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 203), 2023, Yinchuan, China

Abstract

The 0-1knapsack problem is well known to be NP-hard in optimization algorithms. There are many commonly used methods to optimize such problems, such as dynamic programming, enumeration, pruning and bounding, greedy algorithms, backtracking, etc. With the advent of some modern heuristics, such as swarm intelligent optimization algorithms, more options have been provided for solving such problems. The Gene-tic algorithm is one of the most commonly used algorithms. The idea of this paper is as follows: firstly, the mathematical model of 0-1 knapsack problem is given, combining the characteristics of the model, the idea of double structure is introduced, combined with the greedy algorithm, the two-step method of value factor is proposed, and the secondary selection coding is carried out. In the selection operation, recombination is used to improve the convergence of the following algorithm. Compared with the traditional binary genetic algorithm, the genetic algorithm with penalty terms and the Standard Genetic Algorithm (SGA), the convergence of the improved binary genetic algorithm is significantly improved.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Fan Wang, Renjun Yan, and Yu Qiu "Research on hybrid genetic algorithm based on double structure coding", Proc. SPIE 12941, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2023), 129410O (7 December 2023); https://doi.org/10.1117/12.3011779

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KEYWORDS

Genetic algorithms

Mathematical optimization

Genetics

Mathematical modeling

Binary data

Computer programming

Computer simulations

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