2011Unpublished venueRequires access

A simulated annealing- new genetic algorithm and its application

Guangming Lv, Xiaomeng Sun, Jian Wang

Open publisher page 11 citations

Abstract

In this paper, a new kind of algorithm was opposed combined with simulated annealing algorithm and new genetic algorithm. The simulated annealing (SA) method was brought into the genetic algorithm (GA), which combined the two methods into a new global optimization algorithm. The use of SA reduces the stress to choose for GA. Father more, the combination can reduce the search area and avoid the premature convergence problem existing in genetic algorithm, so to improve the convergence of the algorithm. The crossover operator in genetic operation plays a more important role in this algorithm. Through computer simulation, we can see there are advantages in this algorithm compared with traditional genetic algorithm and other pre-existing simulated annealing-genetic algorithm.

About this research paper

What this paper is about

In this paper, a new kind of algorithm was opposed combined with simulated annealing algorithm and new genetic algorithm. The simulated annealing (SA) method was brought into the genetic algorithm (GA), which combined the two methods into a new global optimization algorithm. The use of SA reduces the stress to choose for GA. Father more, the combination can reduce the search area and avoid the premature convergence problem existing in genetic algorithm, so to improve the convergence of the algorithm. The crossover operator in genetic operation plays a more important role in this algorithm. Through computer simulation, we can see there are advantages in this algorithm compared with traditional genetic algorithm and other pre-existing simulated annealing-genetic algorithm.

Why it matters

OpenAlex reports 11 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

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Method / approach

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Main findings

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Available abstract

In this paper, a new kind of algorithm was opposed combined with simulated annealing algorithm and new genetic algorithm. The simulated annealing (SA) method was brought into the genetic algorithm (GA), which combined the two methods into a new global optimization algorithm. The use of SA reduces the stress to choose for GA. Father more, the combination can reduce the search area and avoid the premature convergence problem existing in genetic algorithm, so to improve the convergence of the algorithm. The crossover operator in genetic operation plays a more important role in this algorithm. Through computer simulation, we can see there are advantages in this algorithm compared with traditional genetic algorithm and other pre-existing simulated annealing-genetic algorithm.

Key concepts: Simulated annealing, Crossover, Adaptive simulated annealing, Genetic algorithm, Population-based incremental learning, Computer science, Algorithm, Meta-optimization

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