Results 151 to 160 of about 1,131,016 (198)
Smooth genetic algorithm [PDF]
Journal of Physics A: Mathematical and General, 1994 Summary: An existing family of genetic algorithms, which were designed with discrete and binary variables in mind, has been extended in this paper to handle truly continuous variables. Its close relationships with Monte Carlo methods, the simplex method, simulated annealing and other direct, i.e. derivative-free global optimization algorithms creates a
openaire +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
2008
The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the Travelling Salesman Problem in which a salesman must visit each of n cities once and only once in an optimum order - that which minimises his travelling.
Darryl Charles +3 more
openaire +1 more source
The methods in this chapter were developed in response to the need for general purpose methods for solving complex optimisation problems. A classical problem addressed is the Travelling Salesman Problem in which a salesman must visit each of n cities once and only once in an optimum order - that which minimises his travelling.
Darryl Charles +3 more
openaire +1 more source
2015
The essence of machine learning is the search for the best solution to our problem: to find a classifier which classifies as correctly as possible not only the training examples, but also future examples. Chapter 1 explained the principle of one of the most popular AI-based search techniques, the so-called hill-climbing, and showed how it can be used ...
openaire +2 more sources
The essence of machine learning is the search for the best solution to our problem: to find a classifier which classifies as correctly as possible not only the training examples, but also future examples. Chapter 1 explained the principle of one of the most popular AI-based search techniques, the so-called hill-climbing, and showed how it can be used ...
openaire +2 more sources
The algebra of genetic algorithms [PDF]
Annals of Mathematics and Artificial Intelligence, 1994 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +1 more source
2004
Publisher Summary This chapter reviews the basics of genetic algorithms (GAs), briefly describes the schema theorem and the building block hypothesis, and explains feature selection based on GAs, as one of the most important applications of GAs. GAs differ from classical optimization and search procedures: (1) direct manipulation of a coding, (2 ...
openaire +3 more sources
Publisher Summary This chapter reviews the basics of genetic algorithms (GAs), briefly describes the schema theorem and the building block hypothesis, and explains feature selection based on GAs, as one of the most important applications of GAs. GAs differ from classical optimization and search procedures: (1) direct manipulation of a coding, (2 ...
openaire +3 more sources
European Journal of Operational Research, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
openaire +2 more sources
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yu-Chiun Chiou, Lawrence W. Lan
openaire +2 more sources
Genetic algorithms and evolution
Journal of Theoretical Biology, 1990The genetic algorithm (GA) as developed by Holland (1975, Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press) is an optimization technique based on natural selection. We use a modified version of this technique to investigate which aspects of natural selection make it an efficient search procedure.
Alasdair I. Houston +4 more
openaire +3 more sources
Genetic algorithms in chemometrics
Journal of Chemometrics, 2012This review covers the application of Genetic Algorithms (GAs) in Chemometrics. The first applications of GAs in chemistry date back to the 1970s, and in the last decades, they have been more and more frequently used to solve different kinds of problems, for example, when the objective functions do not possess properties such as continuity ...
A. Niazi, LEARDI, RICCARDO
openaire +3 more sources
The proportional genetic algorithm: Gene expression in a genetic algorithm
Genetic Programming and Evolvable Machines, 2002Summary: We introduce a genetic algorithm (GA) with a new representation method which we call the proportional GA (PGA). The PGA is a multi-character GA that relies on the existence or non-existence of genes to determine the information that is expressed.
Ivan Garibay, Annie S. Wu
openaire +2 more sources
2014
Genetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications. A vast majority of well-known optimization problems have been solved using genetic algorithms. In addition, genetic algorithms are population-based, and many modern evolutionary algorithms are directly based on genetic algorithms or ...
openaire +2 more sources
Genetic algorithms are among the most popular evolutionary algorithms in terms of the diversity of their applications. A vast majority of well-known optimization problems have been solved using genetic algorithms. In addition, genetic algorithms are population-based, and many modern evolutionary algorithms are directly based on genetic algorithms or ...
openaire +2 more sources