Results 261 to 270 of about 123,558 (314)

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid.

Heliyon
Baghoolizadeh M, Jasim DJ, Sajadi SM, Renani RR, Renani MR, Hekmatifar M.   +5 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Evolutionary Algorithms

Information Sciences, 2001
This article broadly introduces evolutionary algorithms and discusses the current trends, both in a historical perspective and with respect to practical outcomes. It then quickly surveys theoretical results and main domains of applications.
Michalewicz, Z., Schoenauer, M.
openaire   +3 more sources

Evolutionary Rao algorithm

Journal of Computational Science, 2021
Abstract This paper proposes an evolutionary  Rao algorithm (ERA) to enhance three state-of-the-art metaheuristic Rao algorithms (Rao-1, Rao-2, Rao-3) by introducing two new schemes. Firstly, the population is split into two sub-populations based on their qualities: high and low, with a particular portion. The high-quality sub-population searches for
Suyanto Suyanto, Agung Toto Wibowo, Said al Faraby, Siti Saadah, Rita Rismala   +4 more
openaire   +1 more source

Evolutionary Algorithms

WIREs Data Mining and Knowledge Discovery, 2014
AbstractEvolutionary algorithm (EA) is an umbrella term used to describe population‐based stochastic direct search algorithms that in some sense mimic natural evolution. Prominent representatives of such algorithms are genetic algorithms, evolution strategies, evolutionary programming, and genetic programming.
Thomas Bartz-Beielstein, Jürgen Branke, Jörn Mehnen, Olaf Mersmann   +3 more
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +2 more sources

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
openaire   +1 more source

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
openaire   +1 more source

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
openaire   +1 more source