Distributed Simulated Annealing for Composite Stock Cutting Problem

Kim, Won-Il; Hong, Chuleui; Kim, Yeong-Joon

doi:10.1007/978-3-540-30501-9_52

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Cited by 2 publications

(2 citation statements)

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“…Mean Field Genetic Algorithm (MGA) is a hybrid algorithm based on Mean Field Annealing (MFA) and Simulated annealing-like Genetic Algorithm (SGA) [8]. MFA has the characteristics of rapid convergence to the equilibrium state while the simulated annealing takes long time to reach the equilibrium state.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Learning Approach based on Mean Field Genetic Algorithms

Hong¹,

Kim

2014

IJSEIA

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Section: Introductionmentioning

confidence: 99%

A Meta-Learning Approach based on Mean Field Genetic Algorithms

Hong¹,

Kim

2014

IJSEIA

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“…The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decisionmaking process or not. Mean Field Genetic Algorithm (MGA) is a hybrid algorithm based on mean field annealing (MFA) and genetic algorithm (GA) [5]. MFA has the characteristics of rapid convergence to the equilibrium state while the simulated annealing (SA) takes long time to reach the equilibrium state.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Learning Approach for Combining Multiple Classifiers

Kim

Hong

2013

Interdisciplinary Research Theory and Technology

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This paper presents an approach for building a multi-classifier system in a Mean Field Genetic Algorithm (MGA)-based inductive learning environment. Multiple base classifiers are combined to build a multi-classifier system. A base classifier consists of a general classifier and a meta-classifier. The general classifier performs regular classification task. The meta-classifier evaluates classification result of its general classifier and decides whether the base classifier participates into a final decision-making process or not. MGA is a hybrid algorithm of Mean Field Annealing (MFA) and Simulated annealinglike Genetic Algorithm (SGA). It combines benefit of rapid convergence property of MFA and effective genetic operations of SGA.

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Distributed Simulated Annealing for Composite Stock Cutting Problem

Cited by 2 publications

References 8 publications

A Meta-Learning Approach based on Mean Field Genetic Algorithms

A Meta-Learning Approach based on Mean Field Genetic Algorithms

A Meta-Learning Approach for Combining Multiple Classifiers

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