论文信息 - The Evolution of Evolutionary Computation

The Evolution of Evolutionary Computation

Evolutionary computation has enjoyed a tremendous growth for at least a decade in both its theoretical foundations and industrial applications. Its scope has gone far beyond binary string optimisation using a simple genetic algorithm. Many research topics in evolutionary computation nowadays are not necessarily ”genetic” or ”evolutionary” in any biological sense. This talk will describe some recent research efforts in addressing several fundamental as well as more applied issues in evolutionary computation. Links with traditional computer science and artificial intelligence will be explored whenever appropriate.

Xin Yao

[1] Xin Yao,et al. Erratum to: Drift analysis and average time complexity of evolutionary algorithms [Artificial Intelligence 127 (2001) 57-85] , 2002, Artif. Intell..

[2] Xin Yao,et al. Using Negative Correlation to Evolve Fault-Tolerant Circuits , 2003, ICES.

[3] Thomas Bäck,et al. An Overview of Evolutionary Computation , 1993, ECML.

[4] Xin Yao,et al. Speciation as automatic categorical modularization , 1997, IEEE Trans. Evol. Comput..

[5] Xin Yao,et al. Making use of population information in evolutionary artificial neural networks , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[6] Xin Yao,et al. From an individual to a population: an analysis of the first hitting time of population-based evolutionary algorithms , 2002, IEEE Trans. Evol. Comput..

[7] Xin Yao,et al. Towards an analytic framework for analysing the computation time of evolutionary algorithms , 2003, Artif. Intell..

[8] Xin Yao,et al. Drift analysis and average time complexity of evolutionary algorithms , 2001, Artif. Intell..

[9] Xin Yao,et al. Evolutionary computation : theory and applications , 1999 .

[10] Xin Yao,et al. Evolutionary ensembles with negative correlation learning , 2000, IEEE Trans. Evol. Comput..