Results 241 to 250 of about 3,008,864 (289)

Evolving evolutionary algorithms using evolutionary algorithms

Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007
A new model for automatic generation of Evolutionary Algorithms (EAs) by evolutionary means is proposed in this paper. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem.
Laura Diosan, Mihai Oltean
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Evolutionary design of Evolutionary Algorithms

Genetic Programming and Evolvable Machines, 2009
Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters
Laura Diosan, Mihai Oltean
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Quasirandom evolutionary algorithms

Proceedings of the 12th annual conference on Genetic and evolutionary computation, 2010
Motivated by recent successful applications of the concept of quasirandomness, we investigate to what extent such ideas can be used in evolutionary computation. To this aim, we propose different variations of the classical (1+1) evolutionary algorithm, all imitating the property that the (1+1) EA over intervals of time touches all bits roughly the same
Benjamin Doerr, Mahmoud Fouz, Carsten Witt   +2 more
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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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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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Evolutionary algorithms

1996
Evolutionary algorithms have been gaining increased attention the past few years because of their versatility and are being successfully applied in several different fields of study. We group under this heading a family of new computing techniques rooted in biological evolution that can be used for solving hard problems.
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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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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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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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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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