GAs tend to zero in on a solution much faster than a neural network and generally are highly predictable in their process time. The advantage of GAs in solving complex problems lies in their ability to "stretch" themselves across a vast solution space in search of the optimal solution. The applications of genetic algorithms can be used to solve problems that we have no clue how to solve.
The power of GAs doesn't result from a complex algorithmic perspective, but rather a relatively simple and widely understood set of concepts. 11. Describe genes and chromosomes as applied to Genetic Algorithms.
The GA's smallest unit is called a gene which represents the smallest unit of information in the problem domain and can be thought of as the basic building block for a possible solution. A series of genes that represent the components of one possible solution to the problem is referred to as a chromosome. The chromosome is represented in computer memory as a bit string of binary digits that ... more.
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