Population structure of heuristic search algorithm based on adaptive partitioning

Authors:
Chang-Wook Han;Jung-Il Park
Affiliations:
School of Electrical Engineering and Computer Science, Yeungnam University, Gyongsan, Gyongbuk, South Korea;School of Electrical Engineering and Computer Science, Yeungnam University, Gyongsan, Gyongbuk, South Korea
Venue:
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Year:
2006

Citing 2
Cited 0

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
An analysis of the behavior of a class of genetic adaptive systems.

An analysis of the behavior of a class of genetic adaptive systems.

Quantified Score

Hi-index	0.01

Visualization

Abstract

A random signal-based learning merged with simulated annealing (SARSL), which is serial algorithm, has been considered by the authors. But the serial nature of SARSL degrades its performance as the complexity of the search space is increasing. To solve this problem, this paper proposes a population structure of SARSL (PSARSL) which enables multi-point search. Moreover, adaptive partitioning method (APM) is used to reduce the optimization time. The validity of the proposed algorithm is conformed by applying it to a complex test function example and a general version of fuzzy controller design.