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Mixed‐Metal Promotion in a Manganese‐Molybdenum Oxynitride as Catalyst to Integrate C─C and C─N Coupling Reactions for the Direct Synthesis of Acetonitrile from Syngas and Ammonia

Advanced Materials, EarlyView.
Transition metal oxy/carbo‐nitrides show great promise as catalysts for sustainable processes. A Mn‐Mo mixed‐metal oxynitride attains remarkable performance for the direct synthesis of acetonitrile, an important commodity chemical, via sequential C─N and C─C coupling from syngas (C1) and ammonia (N1) feedstocks.
M. Elena Martínez‐Monje, Wilson Henao, Tania Rodenas, Patricia Concepción, Virginia Pérez Dieste, Alexander Missyul, Giovanni Agostini, Gonzalo Prieto   +7 more
wiley   +1 more source

Support Vector Machines Trained with Evolutionary Algorithms Employing Kernel Adatron for Large Scale Classification of Protein Structures

Evolutionary Bioinformatics, 2016
Nancy Arana-Daniel, Alberto A. Gallegos, Carlos López-Franco, Alma Y. Alanís, Jacob Morales, Adriana López-Franco   +5 more
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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, Olaf Mersmann, Jörn Mehnen, Jürgen Branke   +3 more
openaire   +3 more sources

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   +2 more sources

Evolutionary Algorithms

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.
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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
B. Prime, T. Hendtlass
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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
openaire   +2 more sources

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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Working with Evolutionary Algorithms [PDF]

, 2003
The main objective of this chapter is to provide practical guidelines for working with EAs. Working with EAs often means comparing different versions experimentally. Guidelines to perform experimental comparisons are therefore given much attention, including the issues of algorithm performance measures, statistics, and benchmark test suites.
Agoston E. Eiben, James C. Smith
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What is an Evolutionary Algorithm?

2003
The most important aim of this chapter is to describe what an evolutionary algorithm is. This description is deliberately based on a unifying view presenting a general scheme that forms the common basis of all evolutionary algorithm (EA) variants. The main components of EAs are discussed, explaining their role and related issues of terminology. This is
Agoston E. Eiben, James C. Smith
openaire   +2 more sources