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Lai X.,South China University of Technology | Lai X.,Shangrao Normal University | Zhou Y.,South China University of Technology | He J.,Aberystwyth University | And 3 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2014

A few experimental investigations have shown that evolutionary algorithms (EAs) are efficient for the minimum label spanning tree (MLST) problem. However, we know little about that in theory. In this paper, we theoretically analyze the performances of the (1+1) EA, a simple version of EA, and a simple multiobjective evolutionary algorithm called GSEMO on the MLST problem. We reveal that for the MLSTbproblem, the (1+1) EA and GSEMO achieve a b+1/2-approximation ratio in expected polynomial runtime with respect to n , the number of nodes, and k , the number of labels. We also find that GSEMO achieves a (2ln n+1) -approximation ratio for the MLST problem in expected polynomial runtime with respect to n and k. At the same time, we show that the (1+1) EA and GSEMO outperform local search algorithms on three instances of the MLST problem. We also construct an instance on which GSEMO outperforms the (1+1) EA. © 1997-2012 IEEE. Source


Li Y.-L.,Sun Yat Sen University | Chen W.-N.,Key Laboratory of Digital Life | Zhang J.,Key Laboratory of Software Technology
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion | Year: 2012

Crossover is a very important operation in current differential evolution (DE) algorithms. The existing crossover strategies in DE show promising effects especially when the algorithms are applied to separable functions. However, the operation fails to work well when applied to the ill-conditioned and inseparable problems because the recombination of good genes is no longer promising for generating better individuals. In this paper, we propose to use the principal component analysis (PCA) technique to rebuild a coordinate system. With this system, the correlations among variables are decreased for the crossover operation of DE and the crossover operation become more efficient. Copyright is held by the author/owner(s). Source


Zhong J.-H.,Sun Yat Sen University | Zhong J.-H.,Key Laboratory of Digital Life | Zhong J.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 2 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation | Year: 2012

Differential Evolution is a new paradigm of evolutionary algorithm which has been widely used to solve nonlinear and complex problems. The performance of DE is mainly dependent on the parameter settings, which relate to not only characteristics of the specific problem but also the evolution state of the algorithm. Hence, determining the suitable parameter settings of DE is a promising but challenging task. This paper presents an enhanced algorithm, namely, the stochastic coding differential evolution, to improve the robustness and efficiency of DE. Instead of encoding each individual as a vector of floating point numbers, the proposed SDE represents each individual by a multivariate normal distribution. In this way, individuals in the population can be more sensible to their surrounding regions and the algorithm can explore the search space region-by-region. In the SDE, a newly designed update operator and a random mutation operator are incorporated to improve the algorithm performance. Traditional DE operators such as the mutation scheme and the crossover operator are also accordingly extended. The proposed SDE has been validated by nine benchmark test functions with different characteristics. Five EAs are compared in the experiment study. The comparison results demonstrate the effectiveness and efficiency of the SDE. © 2012 ACM. Source


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 2 more authors.
GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation Companion | Year: 2012

This paper proposes a novel differential evolution (DE) algorithm with random walk (DE-RW). Random walk is a famous phenomenon universally exists in nature and society. As random walk is an erratic movement that can go in any direction and go to any place, it is likely that this mechanism can be used in search algorithm to bring in diversity. We apply the random walk mechanism into conventional DE variants with different parameters. Experiments are conducted on a set of benchmark functions with different characteristics to demonstrate the advantages of random walk in avoiding local optima. Experimental results show that DE-RWs have general better performance than their corresponding conventional DE variants, especially on multimodal functions. Copyright is held by the author/owner(s). Source


Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Digital Life | Zhan Z.-H.,Key Laboratory of Software Technology | Zhang J.,Sun Yat Sen University | And 4 more authors.
2012 IEEE Congress on Evolutionary Computation, CEC 2012 | Year: 2012

Brain storm optimization (BSO) is a new kind of swarm intelligence algorithm inspired by human creative problem solving process. Human being is the most intelligent organism in the world and the brainstorming process popularly used by them has been demonstrated to be a significant and promising way to create great ideas for problem solving. BSO transplants the brainstorming process in human being into optimization algorithm design and gains successes. BSO generally uses the grouping, replacing, and creating operators to produce ideas as many as possible to approach the problem global optimum generation by generation. In this paper, we propose two novel designs to enhance the conventional BSO performance. The first design of the modified BSO (MBSO) is that it uses a simple grouping method (SGM) in the grouping operator instead of the clustering method to reduce the algorithm computational burden. The second design is that MBSO uses a novel idea difference strategy (IDS) in the creating operator instead of the Gaussian random strategy. The IDS not only contains open minded element to avoid the ideas being trapped by local optima, but also can match the search environment to create better new ideas for problem solving. Experiments have been conducted to illustrate the effectiveness and efficiency of the MBSO algorithm. Moreover, the contributions of SGM and IDS are investigated to show how and why MBSO can perform better than BSO. © 2012 IEEE. Source

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