Search Methods and Optimization Techniques – Section 2

On basis of current literature shows the three main types of traditional or conventional search method: calculus-based, enumerative, and random.^†

· Calculus-based methods are also referred to as gradient methods. These methods use the information about the gradient of the function to guide the direction of search. i.e., it seeks local optima by solving the usually nonlinear set of equations resulting from setting the gradient of the objective function equal to zero. If the derivative of the function cannot be computed, because it is discontinuous, these methods often fail.

· Enumerative methods are a very human kind of search and are fairly straight forward within a finite search space, or at least a discretized infinite search space. The algorithm then starts looking at objective function values at every point in the space, one at a time.

· Random search methods are strictly random walks through the search space while saving the best and have recognized the shortcomings (Calculus-based method has lack of robustness and Enumerative method have lack of efficiency) of above two methods. GA is one such kind of Random search method.

References:

^† David Edward Goldberg (1989), “Genetic Algorithms in Search, Optimization and Machine Learning” Addison-Wesley.

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Search Methods and Optimization Techniques – Section 1

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