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ABSTRACT: An improved Guo Tao algorithm (IGT algorithm) is proposed for solving complicated dynamic function optimization problems,
and a function optimization benchmark problem with constrained condition and two dynamic parameters has been designed. The
results achieved by IGT algorithm have been compared with the results from the Guo Tao algorithm (GT algorithm). It is shown
that the new algorithm (IGT algorithm) provides better results. This preliminarily demonstrates the efficiency of the new
algorithm in complicated dynamic environments.
Wuhan University Journal of Natural Sciences 04/2012; 14(5):404-408.
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Advances in Computation and Intelligence, Third International Symposium, ISICA 2008, Wuhan, China, December 19-21, 2008 Proceedings; 01/2008
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Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings; 01/2007
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Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings; 01/2007
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Advances in Computation and Intelligence, Second International Symposium, ISICA 2007, Wuhan, China, September 21-23, 2007, Proceedings; 01/2007
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ABSTRACT: Dynamic optimisation problems are becoming increasingly important; meanwhile, progress in optimisation techniques and in computational resources are permitting the development of effective systems for dynamic optimisation, resulting in a need for objective methods to evaluate and compare different techniques. The search for effective techniques may be seen as a multi-objective problem, trading off time complexity against effectiveness; hence benchmarks must be able to compare techniques across the Pareto front, not merely at a single point. We propose benchmarks for the dynamic travelling salesman problem, adapted from the CHN-144 benchmark of 144 Chinese cities for the static travelling salesman problem. We provide an example of the use of the benchmark, and illustrate the information that can be gleaned from analysis of the algorithm performance on the benchmarks.
Evolutionary Computation, 2004. CEC2004. Congress on; 07/2004
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Int. J. Comput. Math. 01/2002; 79:523-536.
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GECCO 2002: Proceedings of the Genetic and Evolutionary Computation Conference, New York, USA, 9-13 July 2002; 01/2002
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ABSTRACT: How to discover high-level knowledge such as laws of natural
science in observed data automatically is a very important and difficult
task in scientific research. High level knowledge modeled by ordinary
differential equations (ODES) is discovered in observed dynamic data
automatically by an asynchronous parallel evolutionary algorithm called
AP-HEMA. A numerical example is used to demonstrate the potential of
AP-HEMA. The results show that the dynamic models discovered
automatically in the observed dynamic data by computer can sometimes
compare with models discovered by humans
Computational Intelligence and Multimedia Applications, 2001. ICCIMA 2001. Proceedings. Fourth International Conference on; 02/2001
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ABSTRACT: How to discover high-level knowledge modeled by complicated functions, ordinary differential equations and difference equations in databases automatically is a very important and difficult task in KDD research. In this paper, high-level knowledge modeled by ordinary differential equations (ODEs) is discovered in dynamic data automatically by an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example is used to demonstrate the potential of APEA. The results show that the dynamic models discovered automatically in dynamic data by computer sometimes can compare with the models discovered by human
Technology of Object-Oriented Languages and Systems, 2001. TOOLS 39. 39th International Conference and Exhibition on; 02/2001
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TOOLS USA 2001: Software Technologies for the Age of the Internet, 39th International Conference & Exhibition, Santa Barbara, CA, USA, July 29 - August 3, 2001; 01/2001
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ABSTRACT: Recently Tao Guo (1999) proposed a stochastic search algorithm in
his PhD thesis for solving function optimization problems. He combined
the subspace search method (a general multi-parent recombination
strategy) with the population hill-climbing method. The former keeps a
global search for the overall situation, and the latter maintains the
convergence of the algorithm. Guo's algorithm has many advantages, such
as the simplicity of its structure, the high accuracy of its results,
the wide range of its applications, and the robustness of its use. In
this paper a preliminary theoretical analysis of the algorithm is given
and some numerical experiments are performed using Guo's algorithm to
demonstrate the theoretical results. Three asynchronous parallel
algorithms with different granularities for MIMD machines are designed
by parallelizing Guo's algorithm
Evolutionary Computation, 2000. Proceedings of the 2000 Congress on; 02/2000
