An agent model for binary constraint satisfaction problems

Authors:
Weicai Zhong;Jing Liu;Licheng Jiao
Affiliations:
Institute of Intelligent Information Processing (224#), Xidian University, Xi’an, China;Institute of Intelligent Information Processing (224#), Xidian University, Xi’an, China;Institute of Intelligent Information Processing (224#), Xidian University, Xi’an, China
Venue:
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2005

Citing 3
Cited 0

SAW-ing EAs: adapting the fitness function for solving constrained problems

New ideas in optimization
Comparing evolutionary algorithms on binary constraint satisfaction problems

IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Quantified Score

Hi-index	0.00

Visualization

Abstract

With the intrinsic properties of constraint satisfaction problems (CSPs) in mind, several behaviors are designed for agents by making use of the ability of agents to sense and act on the environment. These behaviors are controlled by means of evolution, so that multiagent evolutionary algorithm for constraint satisfaction problems (MAEA-CSPs) results. To overcome the disadvantages of the general encoding methods, the minimum conflict encoding is also proposed. The experiments use 250 benchmark CSPs to test the performance of MAEA-CSPs, and compare it with four well-defined algorithms. The results show that MAEA-CSPs outperforms the other methods. In addition, the effect of the parameters is analyzed systematically.