Add to ORKGThe focus of this senior thesis is applying different machine learning optimization algorithms to different NP-hard problems and comparing their performances. I used three different algorithms: the genetic algorithm, the ant colony optimization algorithm, and simulated annealing. Each of these I applied to three different NP-hard problems: the traveling salesman problem, the graph coloring problem, and the knapsack problem. This resulted in a total of nine different programs. I then compared the solutions found and the execution times of the different algorithms by applying several different data sets to each of the programs
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony opti...
The interplay between optimization and machine learning is one of the most important developments in...
At present, a significant part of optimization problems, particularly questions of combinatorial opt...
Abstract — The use of genetic algorithms was originally motivated by the astonishing success of thes...
This thesis discusses combinatorial optimization problems, its characteristics and solving methods. ...
: combinatorial optimization is an active field of research in Neural Networks. Since the first atte...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
Machine learning has been a computer sciences buzzword for years. The technology has a lot of potent...
Optimization and machine learning are both extremely active research topics. In this thesis, we expl...
Classical optimization techniques have found widespread use in machine learning. Convex optimization...
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony opti...
The interplay between optimization and machine learning is one of the most important developments in...
At present, a significant part of optimization problems, particularly questions of combinatorial opt...
Abstract — The use of genetic algorithms was originally motivated by the astonishing success of thes...
This thesis discusses combinatorial optimization problems, its characteristics and solving methods. ...
: combinatorial optimization is an active field of research in Neural Networks. Since the first atte...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
Machine learning has recently emerged as a prospective area of investigation for OR in general and s...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
The interplay between optimization and machine learning is one of the most important developments in...
Machine learning has been a computer sciences buzzword for years. The technology has a lot of potent...
Optimization and machine learning are both extremely active research topics. In this thesis, we expl...
Classical optimization techniques have found widespread use in machine learning. Convex optimization...
This thesis deals with four optimization algorithms inspired by nature. It describes ant colony opti...
The interplay between optimization and machine learning is one of the most important developments in...
At present, a significant part of optimization problems, particularly questions of combinatorial opt...