Results 221 to 230 of about 88,043 (267)

Suitability of evolutionary algorithms for evolutionary testing

Proceedings 26th Annual International Computer Software and Applications, 2003
Evolutionary testing is based on the principle of searching for relevant test cases in the input domain of the system under test with the help of evolutionary algorithms. Evolutionary testing enables the complete automation of test case design whenever the test aim can be expressed numerically, e.g.
Joachim Wegener, André Baresel, Harmen Sthamer   +2 more
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Evolving evolutionary algorithms

Proceedings of the 14th annual conference companion on Genetic and evolutionary computation, 2012
This paper proposes a Grammatical Evolution framework to the automatic design of Evolutionary Algorithms. We define a grammar that has the ability to combine components regularly appearing in existing evolutionary algorithms, aiming to achieve novel and fully functional optimization methods. The problem of the Royal Road Functions is used to assess the
Nuno Lourenço 0002, Francisco Baptista Pereira, Ernesto Costa   +2 more
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Trusted Evolutionary Algorithm

2006 IEEE International Conference on Evolutionary Computation, 2006
In both numerical and stochastic optimization methods, surrogate models are often employed in lieu of the expensive high-fidelity models to enhance search efficiency. In gradient-based numerical methods, the trustworthiness of the surrogate models in predicting the fitness improvement is often addressed using ad hoc move limits or a trust region ...
Dudy Lim, Yew-Soon Ong, Yaochu Jin, Bernhard Sendhoff   +3 more
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The immune evolutionary algorithm

1999 Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. Proceedings (Cat. No.99TH8410), 2003
A novel algorithm, the immune evolutionary algorithm (IEA), is proposed based on immune theory in biology, which constructs an immune operator accomplished by two steps: a vaccination and an immune selection. IEA is shown to converge to the global optimum with probability 1.
Lei Wang 0018, Licheng Jiao
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Representations for Evolutionary Algorithms

Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2008
Successful and efficient use of evolutionary algorithms (EA) depends on the choice of the genotype, the problem representation (mapping from genotype to phenotype) and on the choice of search operators that are applied to the genotypes. These choices cannot be made independently of each other.
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Factored Evolutionary Algorithms

IEEE Transactions on Evolutionary Computation, 2017
Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models.
Shane Strasser, John W. Sheppard, Nathan Fortier, Rollie Goodman   +3 more
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Teaching evolutionary algorithms

Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003
Numerous evolutionary computation (EC) courses have been offered at many universities all over the world from the early 90's. However, the field of evolutionary computation is still relatively young, without any standard text nor any standard teaching method. The authors share some experiences in teaching evolutionary courses.
Zbigniew Michalewicz, Maciej Michalewicz
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Integrated Evolutionary Algorithms

2002
The role of sex in evolutionary algorithms, difference between gene and meme are presented. The role of Darwinian selection process, Mendelians genetics, Lamarckian inheritance, Baldwin effect and Dawkins theory of memes are discussed. All states of integrated evolution can be stored in four memories.
Pavel Osmera, Jan Roupec
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Analyses of evolutionary algorithms

2009
Evolutionäre Algorithmen (EAs) werden in der Praxis sehr erfolgreich eingesetzt. Bisher werden die theoretischen Grundlagen von EAs jedoch nicht zufriedenstellend verstanden. Laufzeitanalysen für einfache EAs sollen dieses Verständnis erweitern.
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An evolutionary reincarnation algorithm

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
As there is little or no experimental experience of reincarnation in the natural world, attempts to add a reincarnation metaphor to an evolutionary algorithm must of necessity proceed cautiously. In previous work the authors have established that the reintroduction of previously stored gene values into the population can have a noticeable effect on the
Ben Prime, Tim Hendtlass
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