Results 321 to 330 of about 389,432 (344)
Some of the next articles are maybe not open access.
ACM SIGBIO Newsletter, 1992
Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the
openaire +2 more sources
Genetic Algorithms and Evolution Strategies, the main representatives of a class of algorithms based on the model of natural evolution, are discussed w.r.t. their basic working mechanisms, differences, and application possibilities. The mechanism of self-adaptation of strategy parameters within Evolution Strategies is emphasized and turns out to be the
openaire +2 more sources
Convergence Rates of Evolutionary Algorithms and Parallel Evolutionary Algorithms
2013This chapter discusses the advantages (robustness) and drawbacks (slowness) of algorithms searching the optimum by comparisons between fitness values only. The results are mathematical proofs, but practical implications in terms of speed-up for algorithms applied on parallel machines are presented, as well as practical hints for tuning parallel ...
Fabien Teytaud, Olivier Teytaud
openaire +2 more sources
Evolutionary Algorithms for Aerofoil Design
International Journal of Computational Fluid Dynamics, 1998Abstract This paper wishes lo describe Evolutionary Algorithms as an effective means for the solution of the Aerofoil Design Optimisation in Aerodynamics. Firstly the basic ideas underlying Evolutionary Algorithms arc outlined. Several versions of Evolutionary Algorithms arc briefly described, focussing on their similarities and on their differences as
De Falco I+3 more
openaire +3 more sources
Fuzzy clustering with evolutionary algorithms
International Journal of Intelligent Systems, 1998Objective function-based fuzzy clustering aims at finding a fuzzy partition by optimizing a function that evaluates a (fuzzy) assignment of a given data set to clusters that are characterized by a set of parameters, the so-called prototypes. The iterative optimization technique usually requires the objective function not only to be differentiable, but ...
Klawonn, F., Keller, A.
openaire +2 more sources
Suitability of evolutionary algorithms for evolutionary testing
Proceedings 26th Annual International Computer Software and Applications, 2003Evolutionary 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.
Harmen Sthamer+2 more
openaire +2 more sources
Geometry of evolutionary algorithms
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011The various flavors of Evolutionary Algorithms look very similar when cleared of algorithmically irrelevant differences such as domain of application and phenotype interpretation. Representation-independent algorithmic characteristics like the selection scheme can be freely exchanged between algorithms.
openaire +2 more sources
Designing Evolutionary Algorithms
2000The essential idea of evolutionary problem solving is quite simple. A population of candidate solutions to the task at hand is evolved over successive iterations of random variation and selection. Random variation provides the mechanism for discovering new solutions.
Zbigniew Michalewicz, David B. Fogel
openaire +2 more sources
2017
In the chapter we introduce two evolutionary algorithms, specifically genetic algorithms and the more recent differential evolution algorithm. We describe the key processes of selection, mutation and recombination. We consider both binary and continuous (or real) versions of the genetic algorithm.
George Lindfield, John Penny
openaire +2 more sources
In the chapter we introduce two evolutionary algorithms, specifically genetic algorithms and the more recent differential evolution algorithm. We describe the key processes of selection, mutation and recombination. We consider both binary and continuous (or real) versions of the genetic algorithm.
George Lindfield, John Penny
openaire +2 more sources
Concepts of Evolutionary Modeling and Evolutionary Algorithms
2001This book is about evolutionary algorithms as applied to spatial and geographic phenomena. Why are we writing this book? Do these new algorithms deliver solutions to our modeling and data analysis problems that conventional methods cannot handle? Or will they just fade away, as have so many other “new” ideas from the past, some eventually finding their
Roman M. Krzanowski, Jonathan Raper
openaire +1 more source
Noisy intermediate-scale quantum algorithms
Reviews of Modern Physics, 2022Kishor Bharti+2 more
exaly