What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
Information landscapes and problem hardness
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
A Study of Fitness Distance Correlation as a Difficulty Measure in Genetic Programming
Evolutionary Computation
Fitness-proportional negative slope coefficient as a hardness measure for genetic algorithms
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Designing optimal PID controller with genetic algorithm in view of controller location in the plant
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
The new negative slope coefficient measure
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Application of genetic algorithm and neural network in forecasting with good data
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Negative slope coefficient: a measure to characterize genetic programming fitness landscapes
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Comparison of a crossover operator in binary-coded genetic algorithms
WSEAS Transactions on Computers
Image region segmentation based on the virtual edge current in digital images
NEHIPISIC'11 Proceeding of 10th WSEAS international conference on electronics, hardware, wireless and optical communications, and 10th WSEAS international conference on signal processing, robotics and automation, and 3rd WSEAS international conference on nanotechnology, and 2nd WSEAS international conference on Plasma-fusion-nuclear physics
WSEAS Transactions on Computers
| Hi-index | 0.00 |
Genetic algorithms (GA) have been successfully applied to various problems, both artificial as well as real-world problems. When working with GAs it is important to know those those kinds of situations when they will not find the optimal solution. In other words, to recognize problems that are difficult for a GA to solve. There are various reasons why GAs will not converge to optimal solutions. By combining one or more of these reasons a problem can become a GA-hard problem. Today, there are numerous methods for solving GA-hard problems; every measure has its specific advantages and drawbacks. In this work the effectiveness of one of these measures is evaluated, namely the Negative Slope Coefficient (NSC) measure. A different measure is proposed, called the New Negative Slope Coefficient (NNSC) measure, which aims to address certain drawbacks of the original method. Possible guidelines for further development of this, and comparable methods are proposed.