ABSTRACT
In this paper, an improved collaborative optimized genetic algorithm (CGA) inspired from the modulation mechanism of neuroendocrine-immune system is presented. The CGA has several features as follows. The first is that the parent individuals are not involved in the copy process. The second is that more excellent individuals may be produced due to the adaptive crossover and variation probability based on the hormone modulation. In order to examine its performance, firstly, two typical test functions are selected as the simulation models. Then CGA is applied to an intelligent controller based on the modulation of epinephrine (EIC). The simulation results show that the CGA has quicker convergence rate and higher searching precision than that of immune genetic algorithm and normal genetic algorithm, and the EIC optimized has satisfactory control performance
- Ding Y.S., Natural Computation and Network Intelligence {M}, Shanghai Jiaotong University Press, (Jan. 2008)Google Scholar
- Kim B-Y., Nam G. J., Ryu H. S. and Lee J. W., Optimization of filling process in RTM using genetic algorithm {J}. Korea-Australia Rheology Journal, 12(1): 83--92(2000).Google Scholar
- Su X. H., Yang B., and Wang Y.D., A Genetic Algorithm Based on Evolutionarily Stable Strategy {J}. Journal of Software, 14(11): 1863--1868(2003).Google Scholar
- Khaled B. and Faouzi T., Genetic algorithm for the design of a class fuzzy controller: an alternative approach {J}. IEEE Trans. on Fuzzy Systems, 8(4): 398--405(2000). Google ScholarDigital Library
- Fleming P. J. and Purshouse R. C., Evolutionary algorithms in control systems engineering: a survey {J}. Control Engineering Practice, 10(11): 1223--1241(2002).Google ScholarCross Ref
- Lam H. K., Ling S. H., F. Leung H. F., et al. Optimal and stable fuzzy controllers for nonlinear systems subject to parameter uncertainties using genetic algorithms {J}. IEEE Transactions on Industrial Electronics, 51(1): 172-- 182(2004).Google ScholarCross Ref
- Farhy L. S., Modeling of oscillations of endocrine networks with feedback {J}. Methods Enzymol, 384: 54--81(2004).Google ScholarCross Ref
- Brazzini B., Ghersetich I., Hercogova J., and Lotti T., the Neuro-immuno-cutaneousendocrine network: relationship between mind and skin, Dermatologic Therapy, 16: 123--131(2003).Google ScholarCross Ref
- Payne J. K., A neuroendocrine-based regulatory fatigue model, BIOLOGICAL RESEARCH FOR NURSING, 6(2): 141--150(2004).Google ScholarCross Ref
- Ding Y.S., Computation Intelligence: Theory, techniques and applications, Scientific Press, (Aug. 2004)Google Scholar
- Liu B, Ding Y. S., A novel intelligent controller based on hormone modulation of neuroendocrine system {J}, computer simulation, 23(2): 129--132(2006).Google Scholar
- Ding Y.-S., Liu B., Ren L.-H., Intelligent decoupling control system inspired from modulation of the growth hormone in neuroendocrine system, Dynamics of Continuous, Discrete & Impulsive Systems, Series B: Applications & Algorithms, 14(5): 679--693(2007).Google Scholar
Index Terms
- A collaborative optimized genetic algorithm based on regulation mechanism of neuroendocrine-immune system
Recommendations
A new crossover mechanism for genetic algorithms with variable-length chromosomes for path optimization problems
A new crossover mechanism containing two operators is proposed.The new mechanism produces more cross point pairs than same point crossover.Our novel genetic algorithm has a desirable performance. Genetic Algorithm (GA) has found wide application in path ...
Crossover Consideration in Genetic Algorithm
ICMLSC '24: Proceedings of the 2024 8th International Conference on Machine Learning and Soft ComputingCrossover is an important process in genetic algorithms. This process will swap genes between the chromosomes of the parents. The results from the crossover process may not be better than those of the parents, which affect the result of the genetic ...
Enhanced Genetic Algorithm Applied for Global Optimization
ICONIP 2015: Proceeings, Part II, of the 22nd International Conference on Neural Information Processing - Volume 9490Conventional genetic algorithm GA has several drawbacks such as premature convergence and incapable of fine tuning around potential region. Thus, new enhanced GA that focuses on new search, crossover and elitism strategy is proposed in this study. It ...
Comments