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A new multi-objective supervised clustering genetic algorithm is proposed in this paper. Training samples are supervised clustered by attribute similarity and class label. The number and center of class family can be determined automatically by using the fitness vector function. The two key elements have optimization nature and can be unaffected by subjective factors. Use the nearest neighbor rule...
A new classification learning system based on multi-objective GA is proposed in this paper. Firstly, the continuous attributes of samples are made discretion with a supervised segmentation method, so generalization and intelligibility of machine learning are improved. Moreover, comparison and selection mechanism based on partial order in set theory are infused into multi-objective GA. They enhance...
A new neuron classification algorithm called NCA is proposed in the paper.The neurons of NCA divided the sample space non-linearly and adjusted themselves to cover the sample space optimally.After the noise data processed,the generalization of NCA has been enhanced.In the forecast,the introduction of law of attraction not only overcome the lack of Euclidean distance but also increases the accuracy...
A new multi-objective supervised clustering genetic algorithm is proposed in this paper. Training samples are supervised clustered by attribute similarity and class label. The number and center of class family can be determined automatically by using the fitness vector function. The two key elements have optimization nature and can be unaffected by subjective factors. Use the nearest neighbor rule...
A new immune clone algorithm is proposed for coping with the multi-objective fixed-charged transportation optimization problem (mfcTP) in the paper. In terms of this new algorithm base on the vector affinity, we firstly make the sum of active and fixed-charged order by ascending and greedy algorithm infused into the antibody decoding, sequentially enhancing the intelligent learning ability of antibody;...
The knapsack problem (KP) is a classical NP problem. The multi-objective knapsack problem (MKP) is more difficult than KP. The fitness vector function is firstly introduced, that can solve the problem of non-convex solutions, which is difficult for the common aggregation function method. Then, with the master-slave multi-population cooperation method, the cooperation between the global exploration...
In the multi-objective transportation (MOT) optimization problems, it is quite necessary to consider the tradeoff between all conflictive sub-objectives, consequently it leads to difficulties in solving. So this paper proposed a new Fuzzy Multi-Population Cooperative Genetic Algorithm, called fmc-GA. We firstly infuse the combination of fuzzy rule, which is convenient to express the explicit knowledge,...
For coping with the multi-objective fixed-charged transportation optimization problem (mfcTP) in SCM a new fuzzy rules-based particle swarm optimization algorithm called Fuzzy-PSO is proposed in the paper. In terms of this new algorithm base on the fitness vector function, we firstly construct the fuzzy rule base which is convenient to express the explicit knowledge, and then apply the fuzzy rulers...
A new fuzzy multi-population cooperative immune clone algorithm, called fmcica for the multi-objective optimization problems is proposed in this paper. We firstly make both of fuzzy rules and greedy algorithm infuse into the antibody decoding, sequentially enhancing the intelligent learning ability of antibody; And with the introducing of concepts of the vector affinity it can solve the difficult...
For coping with the multi-objective fixed-charged transportation optimization problem (mfcTP) a new fuzzy rules-based evolutionary algorithm called Fuzzy-EA is proposed in the paper. In terms of this new algorithm base on the fitness vector function, we firstly construct the fuzzy rule base which is convenient to express the explicit knowledge, and then apply the fuzzy rulers to control the process...
This paper proposes an improved genetic algorithm, it keeps the population diversity by similarity checks on the population before selection, and the algorithm solves the early-maturing problem of the population evolution, and proposes a formula for mutation probability related with similarity rate and iteration times. The algorithm not only maintains a good diversity of population, but also guarantees...
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