Fuzzy Clustering and Genetic Algorithms - Biomedical Signal Analysis: Contemporary Methods and Applications - page 240

7.5 Genetic Algorithms

Basic aspects and operations

Genetic algorithms (GA) are simple heuristic optimization tools for both
continuous and discrete variables. These tools provide near-global opti-
mal values even for poorly behaved functions. Compared to traditional
optimization techniques, GAs have softer mathematical requirements by
removing the restrictions on allowable models and error laws. In return,
“softer” solutions to the optimization problem are provided that never-
theless are very good.
Their most important characteristics are the following:

• Parallel-search procedures: implementation on parallel-processing

computers, ensuring fast computations.
• Stochastic nature: avoid local minima, and thus desirable for practical

optimization problems.
•

Applications: continuous and discrete optimization problems.

Genetic algorithms are, like neural networks, biologically inspired
and are based on the application of the principles of “Darwinian natural
selection” to a population of numerical representations of the solution
domain. The natural evolution is emulated by allowing solutions to
reproduce, creating offsprings of them, and allowing only the fittest
to survive. Average fitness improves over generations, although some
offsprings may not be improved compared to the previous generation,
such that the best (fittest) solution is close to the global optimum.
Let's look again at the definition of a GA. In a strict sense, the
classical GA is based on the original work of John Holland in 1975 [116].
This novel evolution-inspired paradigm - known also as the canonical
genetic algorithm - is still a relevant research topic. In a more detailed
sense, the GA represents a solution (population)-based model which
employs selection, mutation, and recombination operators to generate
new data points (offsprings) in a search space [282]. There are several GA
models known in the literature, most of them designed as optimization
tools for several applications in medical imaging. A very important one
- the edge detection - will be reviewed in this chapter.
In summary, GAs differ from classical optimization and search pro-
cedures by (1) direct manipulation of a coding, (2) search from a pop-