Genetic and Evolutionary Computation - MedicalApplications
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Genetic and Evolutionary Computation: Medical Applications provides an overview of the range of GEC techniques being applied to medicine and healthcare in a context that is relevant not only for existing GEC practitioners but also those from other disciplines, particularly health professionals. There is rapidly increasing interest in applying evolutionary computation to problems in medicine, but to date no text that introduces evolutionary computation in a medical context. By explaining the basic introductory theory, typical application areas and detailed implementation in one coherent volume, this book will appeal to a wide audience from software developers to medical scientists.

Centred around a set of nine case studies on the application of GEC to different areas of medicine, the book offers an overview of applications of GEC to medicine, describes applications in which GEC is used to analyse medical images and data sets, derive advanced models, and suggest diagnoses and treatments, finally providing hints about possible future advancements of genetic and evolutionary computation in medicine.

  • Explores the rapidly growing area of genetic and evolutionary computation in context of its viable and exciting payoffs in the field of medical applications.
  • Explains the underlying theory, typical applications and detailed implementation.
  • Includes general sections about the applications of GEC to medicine and their expected future developments, as well as specific sections on applications of GEC to medical imaging, analysis of medical data sets, advanced modelling, diagnosis and treatment.
  • Features a wide range of tables, illustrations diagrams and photographs.


Stephen Smith, Department of Electronics, University of York, UK
Stephen Smith is a senior lecturer within the Department of Electronics at the University of York. His research interests include evolutionary algorithms and assisted clinical diagnosis. He is co-organiser of the annual GECCO (Genetic and Evolutionary Computation Conference), and co-workshop organiser for the Medical Applications of Genetic and Evolutionary Computation Workshop. His editorial experience includes current service as subject area editor for the Journal of Systems Architecture and guest editor for a special issue of the BioSystems journal.

Stefano Cagnoni, Università degli Studi di Parma, Italy
Stefano Cagnoni is an associate professor in the department of computer engineering at the University of Parma. His research interests are in the fields of computer vision, robotics, evolutionary computation and neural networks. He is secretary of the Italian Association for Artificial Intelligence and Co-chairman of EvoIASP, the EvoNet working group on applications of Evolutionary Computation to Image and Signal Processing. He is co-editor of Genetic and Evolutionary Computation for Image Processing and Analysis, soon to publish with Hindawi Press.


About the Editors.

List of Contributors.

1 Introduction.

2 Evolutionary Computation: A Brief Overview (Stefano Cagnoni and Leonardo Vanneschi).

2.1 Introduction.

2.2 Evolutionary Computation Paradigms.

2.2.1 Genetic Algorithms.

2.2.2 Evolution Strategies.

2.2.3 Evolutionary Programming.

2.2.4 Genetic Programming.

2.2.5 Other Evolutionary Techniques.

2.2.6 Theory of Evolutionary Algorithms.

2.3 Conclusions.

3 A Review of Medical Applications of Genetic and Evolutionary Computation (Stephen L. Smith).

3.1 Medical Imaging and Signal Processing.

3.1.1 Overview.

3.1.2 Image Segmentation.

3.1.3 Image Registration, Reconstruction and Correction.

3.1.4 Other Applications.

3.2 Data Mining Medical Data and Patient Records.

3.3 Clinical Expert Systems and Knowledge-based Systems.

3.4 Modelling and Simulation of Medical Processes.

3.5 Clinical Diagnosis and Therapy.

4 Applications of GEC in Medical Imaging.

4.1 Evolutionary Deformable Models for Medical Image Segmentation: A Genetic Algorithm Approach to Optimizing Learned, Intuitive, and Localized Medial-based Shape Deformation (Chris McIntosh and Ghassan Hamarneh).

4.1.1 Introduction. Statistically Constrained Localized and Intuitive Deformations. Genetic Algorithms.

4.1.2 Methods. Population Representation. Encoding the Weights for GAs. Mutations and Crossovers. Calculating the Fitness of Members of the GA Population.

4.1.3 Results.

4.1.4 Conclusions.

4.2 Feature Selection for the Classification of Microcalcifications in Digital Mammograms using Genetic Algorithms, Sequential Search and Class Separability (Santiago E. Conant-Pablos, Rolando R. Hernández-Cisneros, and Hugo Terashima-Marín).

4.2.1 Introduction.

4.2.2 Methodology. Pre-processing. Detection of Potential Microcalcifications (Signals). Classification of Signals into Microcalcifications. Detection of Microcalcification Clusters. Classification of Microcalcification Clusters into Benign and Malignant.

4.2.3 Experiments and Results. From Pre-processing to Signal Extraction. Classification of Signals into Microcalcifications. Microcalcification Clusters Detection and Classification.

4.2.4 Conclusions and Future Work.

4.3 Hybrid Detection of Features within the Retinal Fundus using a Genetic Algorithm (Vitoantonio Bevilacqua, Lucia Cariello, Simona Cambo, Domenico Daleno, and Giuseppe Mastronardi).

4.3.1 Introduction.

4.3.2 Acquisition and Processing of Retinal Fundus Images. Retinal Image Acquisition. Image Processing.

4.3.3 Previous Work.

4.3.4 Implementation. Vasculature Extraction. A Genetic Algorithm for Edge Extraction. Skeletonization Process. Experimental Results.

5 New Analysis of Medical Data Sets using GEC.

5.1 Analysis and Classification ofMammography Reports using Maximum Variation Sampling (Robert M. Patton, Barbara G. Beckerman, and Thomas E. Potok).

5.1.1 Introduction.

5.1.2 Background.

5.1.3 Related Works.

5.1.4 Maximum Variation Sampling.

5.1.5 Data.

5.1.6 Tests.

5.1.7 Results & Discussion.

5.1.8 Summary.

5.2 An Interactive Search for Rules in Medical Data using Multiobjective Evolutionary Algorithms (Daniela Zaharie, D. Lungeanu, and Flavia Zamfirache).

5.2.1 Medical Data Mining.

5.2.2 Measures for Evaluating the Rules Quality. Accuracy Measures. Comprehensibility Measures. Interestingness Measures.

5.2.3 Evolutionary Approaches in Rules Mining.

5.2.4 An Interactive Multiobjective Evolutionary Algorithm for Rules Mining. Rules Encoding. Reproduction Operators. Selection and Archiving. User Guided Evolutionary Search.

5.2.5 Experiments in Medical Rules Mining. Impact of User Interaction.

5.2.6 Conclusions.

5.3 Genetic Programming for Exploring Medical Data using Visual Spaces (Julio J. Valdés, Alan J. Barton, and Robert Orchard).

5.3.1 Introduction.

5.3.2 Visual Spaces. Visual Space Realization. Visual Space Taxonomy. Visual Space Geometries. Visual Space Interpretation Taxonomy. Visual Space Characteristics Examination. Visual Space Mapping Taxonomy. Visual Space Mapping Computation.

5.3.3 Experimental Settings. Implicit Classical Algorithm Settings. Explicit GEP Algorithm Settings.

5.3.4 Medical Examples. Data Space Examples. Semantic Space Examples.

5.3.5 Future Directions.

6 Advanced Modelling, Diagnosis and Treatment using GEC.

6.1 Objective Assessment of Visuo-spatial Ability using Implicit Context Representation Cartesian Genetic Programming (Michael A. Lones and Stephen L. Smith).

6.1.1 Introduction.

6.1.2 Evaluation of Visuo-spatial Ability.

6.1.3 Implicit Context Representation CGP.

6.1.4 Methodology. Data Collection. Evaluation. Parameter Settings.

6.1.5 Results.

6.1.6 Conclusions.

6.2 Towards an Alternative to Magnetic Resonance Imaging for Vocal Tract Shape Measurement using the Principles of Evolution (David M. Howard, Andy M. Tyrrell, and Crispin Cooper).

6.2.1 Introduction.

6.2.2 Oral Tract Shape Evolution.

6.2.3 Recording the Target Vowels.

6.2.4 Evolving Oral Tract Shapes.

6.2.5 Results. Oral Tract Areas. Spectral Comparisons.

6.2.6 Conclusions.

6.3 How Genetic Algorithms can Improve Pacemaker Efficiency (Laurent Dumas and Linda El Alaoui).

6.3.1 Introduction.

6.3.2 Modeling of the Electrical Activity of the Heart.

6.3.3 The Optimization Principles. The Cost Function. The Optimization Algorithm. A New Genetic Algorithm with a Surrogate Model. Results of AGA on Test Functions.

6.3.4 A Simplified Test Case for a Pacemaker Optimization. Description of the Test Case. Numerical Results.

6.3.5 Conclusion.

7 The Future for Genetic and Evolutionary Computation in Medicine: Opportunities, Challenges and Rewards.

7.1 Opportunities.

7.2 Challenges.

7.3 Rewards.

7.4 The Future for Genetic and Evolutionary Computation in Medicine.

Appendix: Introductory Books and Useful Links.