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Evolutionary computation in medicine: an overview.

C A Peña-Reyes¹, M Sipper

¹Logic Systems Laboratory, Swiss Federal Institute of Technology, IN-Ecublens, CH-1015, Lausanne, Switzerland. carlos.pena@epfl.ch

Artificial Intelligence in Medicine

|April 18, 2000

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Evolutionary computation, inspired by natural evolution, offers powerful methods for solving complex medical problems. This paper reviews six algorithms and their applications in areas like diagnosis and medical imaging.

Area of Science:

Computational intelligence
Bio-inspired computing
Medical informatics

Background:

Evolutionary computation (EC) comprises algorithms mimicking natural evolution to tackle complex challenges.
These techniques are increasingly applied across various scientific and engineering disciplines.

Purpose of the Study:

To provide a comprehensive overview of evolutionary computation methodologies.
To explore the application of EC in diverse medical domains.
To present a structured bibliography of EC in medicine.

Main Methods:

Detailed explanation of six core EC algorithm types: genetic algorithms, genetic programming, evolution strategies, evolutionary programming, classifier systems, and hybrid systems.
Categorization of EC applications within medical problem-solving.

Related Experiment Videos

Bibliographic compilation based on medical task and EC technique.

Main Results:

Demonstration of EC's versatility in addressing medical challenges such as diagnosis, prognosis, medical imaging, signal processing, treatment planning, and scheduling.
Identification of specific EC techniques suited for particular medical applications.
A classified bibliography serving as a resource for researchers.

Conclusions:

Evolutionary computation provides a robust framework for advancing medical problem-solving.
The reviewed EC techniques offer innovative solutions for complex medical data analysis and decision-making.
This work facilitates further research and application of EC in the medical field.