A class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover.
Abstract |
This paper focuses on classification problems, and in particular on the evolution of ARTMAP architectures using genetic algorithms, with the objective of improving generalization performance and alleviating the adaptive ... |
|
Abstract |
The Exchange Heuristic (EH) has demonstrated superior results compared with other RCS methods in solving Resource Constrained Scheduling (RCS) problems. Selecting the most promising target constitutes the success of EH. The ... |
|
Abstract |
In this paper, the integration of artificial neural networks and genetic algorithms is explored for solving uncured composite stock cutting problem, which is an NP-complete problem. The input patterns can be either ... |
|
Abstract |
Engineering design is a complex activity, and is heavily reliant on the know-how of engineering designers. Hence, capturing, storing, and reusing design information, design intent, and underlining design knowledge to support ... |
|
Abstract |
Properly selected transformation methods obtain the most significant characteristics of metal cutting data efficiently and simplify the classification. Wavelet Transformation (WT) and Neural Networks (NN) combination was ... |
|
Abstract |
We evaluate the performance of a heterogeneous mixture of neural network algorithms for predicting the exchange-traded fund DIA. A genetic algorithm is utilized to find the best mixture of neural networks, the topology of ... |
|