An optimization algorithm is a numerical method or algorithm for finding a value x such that f(x) is as small (or as large) as possible, for a given function f, possibly with some constraints on x. Here, x can be a scalar or vector of continuous or discrete values. If x is continuous, then the study of the algorithm is part of numerical analysis†.
In general the optimization techniques may be classified as: Exact methods and Heuristic methods.
Exact methods like the branch and bound method are only of theoretical relevance due to their exponential runtime complexity.
Heuristic methods also referred as Combinatorial optimization is a branch of optimization in applied mathematics and computer science, related to operations research, algorithm theory and computational complexity theory that sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Combinatorial optimization algorithms solve instances of problems that are believed to be hard in general, by exploring the usually-large solution space of these instances. Combinatorial optimization algorithms achieve this by reducing the effective size of the space, and by exploring the space efficiently.‡
Heuristic methods such as Simulated Annealing, Ant Colony Optimisation, Tabu Search, Evolutionary Algorithm, Local search, etc… are some of the developing methods. On these Genetic Algorithm is classified under Evolutionary Algorithm.
References:
† Category: Optimization algorithms - Wikipedia, the free encyclopedia, last modified 02:43, 12 February 2006, http://en.wikipedia.org/wiki/Category:Optimization_algorithms
‡Genetic algorithm - Wikipedia, the free encyclopedia, last modified 00:36, 15 February 2006, http://en.wikipedia.org/wiki/ Genetic_algorithm
Related Posts:
Search Methods and Optimization Techniques – Section 1
Search Methods and Optimization Techniques – Section 2
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