**8. Conclusion**

Based on the results of the experiments, the strategies evolved with self adaptive genetic algorithm achieved the most ideal result in terms of success rate and average payoff in an online auction environment setting. The strategies have also achieved a higher average fitness function during the evolution process.

The result in Figure 20, 21, 22 and Table 10 confirmed this conclusion by empirically proving that self adaptive genetic algorithm can evolve better bidding strategies compared to the other genetic algorithm disciplines. Among these different methods, the self-adaptation outperformed all of the other methods due to the nature of the method. In order to achieve better bidding strategies, the self-adaptation crossover and mutation scheme can be used to ensure better bidding strategies which in turn produces higher success rate, average fitness and average payoff.

Further investigation can be conducted by evolving the bidding strategies with two other evolution methods which are the evolution strategies and evolution programming. Evolving the bidding strategies with the evolution programming and evolution strategies may generate interesting result which different from genetic algorithm. A comparison between performances the evolutions strategies, evolution programming and genetic algorithm may produce interesting results.

### **9. References**


286 Bio-Inspired Computational Algorithms and Their Applications

better effective strategies compared to the other strategies evolved for other disciplines and

SA Benchmark Newly Discovered Static Rate DDA Success Rate ⊕ ⊕ ⊕ Average Payoff ⊕ ⊕ ⊕ Table 10. P value for the comparison between different disciplines in term of success rate

The symbol ⊕ in Table 10 indicates that the P-value is less than 0.05 and has significant improvement. The result of P value in the t-test in Table 10 shows the improvement generated by the self-adaptation is more significant compared to the other disciplines. Hence, it can be confirmed that self-adaptation is the best discipline in improving the

Based on the results of the experiments, the strategies evolved with self adaptive genetic algorithm achieved the most ideal result in terms of success rate and average payoff in an online auction environment setting. The strategies have also achieved a higher average

The result in Figure 20, 21, 22 and Table 10 confirmed this conclusion by empirically proving that self adaptive genetic algorithm can evolve better bidding strategies compared to the other genetic algorithm disciplines. Among these different methods, the self-adaptation outperformed all of the other methods due to the nature of the method. In order to achieve better bidding strategies, the self-adaptation crossover and mutation scheme can be used to ensure better bidding strategies which in turn produces higher success rate, average fitness

Further investigation can be conducted by evolving the bidding strategies with two other evolution methods which are the evolution strategies and evolution programming. Evolving the bidding strategies with the evolution programming and evolution strategies may generate interesting result which different from genetic algorithm. A comparison between performances the evolutions strategies, evolution programming and genetic algorithm may

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**8. Conclusion** 

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**14** 

*China* 

, Han Jun and Guo He

**Mining Frequent Itemsets over Recent** 

Zhou Yong\*

*School of Software of Dalian University of Technology, Dalian* 

**Data Stream Based on Genetic Algorithm** 

Data stream is massive sequence of data elements generated at a rapid rate which is characterized by continuously flowing, high arrival rate, unbounded size of data and realtime query requests. The knowledge embedded in a data stream is more likely to be changed as time goes by. Identifying the recent change of a data stream, especially for an online data stream, can provide valuable information for the analysis of the data stream. Frequent patterns on a data stream can provide an important basis for decision making and applications. Because of the data stream's fluidity and continuity, the information of

Mining over data streams is one of the most interesting issues of data mining in recent years. Online mining of data streams is an important technique to handle real-world applications, such as traffic flow management, stock tickers monitoring and analysis, wireless communication management, etc. In most of the data stream applications, users tend to pay more attention to the mode information of the recent data stream. Therefore, mining frequent patterns in recent data stream is a challenging work. The mining process should have one-pass algorithm, high efficiency of updating, limited space cost and online response of queries. However, most of mining algorithms or frequency approximation algorithms over a data stream could not have high efficiency to differentiate the information of recently generated data elements from the obsolete information of old data elements which may be

Many previous studies contributed to efficient mining of the frequent itemsets over the streams. Generally, three processing models are used which are the landmark model, the sliding window model and the damped model[1]. The landmark model analyzes the stream in a particular window, which starts from a fixed timestamp called landmark and ends up with the current timestamp. For the sliding window model case, the mining process is performed over a sliding window of a fixed length. Based on the sliding window model, the oldest data is pruned immediately when a new data arrives. The damped model uses the entire stream to compute the frequency with a decay factor *d*, which makes the recent data

\* Supported by Fundamental Research Funds for the Central Universities No. DUT10JR15

**1. Introduction** 

frequent patterns changes with the new data coming.

no longer useful or possibly invalid at present.

more important than the previous ones.

