Supporting Breast Cancer Diagnosis with Multi-objective Genetic Algorithm for Outlier Detection

2017 ◽  
pp. 304-315 ◽  
Author(s):  
Agnieszka Duraj ◽  
Lukasz Chomatek
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Outlier Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Self-Adapting Intelligent Neural Systems Using Evolutionary Techniques

2006 ◽  
pp. 94-115
Author(s):  
Daniel Manrique ◽  
Juan Rios ◽  
Alfonso Rodriguez-Paton
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Cancer Diagnosis ◽  
Laboratory Tests ◽  
Breast Cancer Diagnosis ◽  
Search Space ◽  
Neural Systems ◽  
Multilayer Perceptrons ◽  
Evolutionary System ◽  
Real World Problem

This chapter describes genetic algorithm-based evolutionary techniques for automatically constructing intelligent neural systems. These techniques can be used to build and train multilayer perceptrons with the simplest architecture. These neural networks are usually designed using binary-coded genetic algorithms. The authors show how the basic architectures codification method, which uses an algebra-based codification, employs a shorter string length and voids illegal architectures in the search space. The networks are trained using real number codification. The morphological crossover operator is presented and compared to other important real-coded crossover operators. The purpose is to understand that the combination of all these techniques results in an evolutionary system, which self-adaptively constructs intelligent neural systems to solve a problem given as a set of training patterns. To do so, the evolutionary system is applied in laboratory tests and to a real-world problem: breast cancer diagnosis.

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