Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
An Adaptive Evolutionary Algorithm for Numerical Optimization
SEAL'96 Selected papers from the First Asia-Pacific Conference on Simulated Evolution and Learning
Learning probability distributions in continuous evolutionary algorithms– a comparative review
Natural Computing: an international journal
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Microgenetic algorithms as generalized hill-climbing operators forGA optimization
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Journal of Global Optimization
An improved multi-agent genetic algorithm for numerical optimization
Natural Computing: an international journal
Global optimization using a multipoint type quasi-chaotic optimization method
Applied Soft Computing
Information Sciences: an International Journal
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Zhong et al. (2004 [IEEE Trans. on Systems, Man and Cybernetics (Part B), 34: 1128---1141]) proposed the multiagent genetic algorithm (MAGA) in their publication titled "A multiagent genetic algorithm for global numerical optimization". The MAGA exploits the known characteristics of some benchmark functions to achieve outstanding results. For example, the MAGA exploits the fact that all variables have the same numerical value at the global optimum and the same upper and lower bounds to solve several 100 dimensional and 1000 dimensional benchmark problems with high precision requiring on average 7000 and 16,000 function evaluations respectively. In this paper, we evaluate the performance of the MAGA experimentally1 and demonstrate that the performance of the MAGA significantly deteriorates when the relative positions of the variables at the global optimal point are shifted with respect to the search ranges.