Scala for Machine Learning

Advantages and risks of genetic algorithms

Now, it should be clear that genetic algorithms provide scientists with a powerful toolbox with which to optimize problems that:

• Are poorly understood.
• May have more than one good enough solution.
• Have discrete, discontinuous, and nondifferentiable functions.
• Can be easily integrated with the rules engine and knowledge bases (for example, learning classifiers systems).
• Do not require deep domain knowledge. The genetic algorithm generates new solution candidates through genetic operators. The initial population does not have to contain the fittest solution.
• Do not require knowledge of numerical methods such as the Newton-Raphson, conjugate gradient, or BFGS as optimization techniques, which frighten those with ...