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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization (Mixed media product, 2009 ed.)
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Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization (Mixed media product, 2009 ed.)
Series: Studies in Computational Intelligence, 175
Expected to ship within 10 - 15 working days
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What is combinatorial optimization? Traditionally, a problem is
considered to be c- binatorial if its set of feasible solutions is
both ?nite and discrete, i. e. , enumerable. For example, the
traveling salesman problem asks in what order a salesman should
visit the cities in his territory if he wants to minimize his total
mileage (see Sect. 2. 2. 2). The traveling salesman problem's
feasible solutions - permutations of city labels - c- prise a
?nite, discrete set. By contrast, Differential Evolution was
originally designed to optimize functions de?ned on real spaces.
Unlike combinatorial problems, the set of feasible solutions for
real parameter optimization is continuous. Although Differential
Evolution operates internally with ?oating-point precision, it has
been applied with success to many numerical optimization problems
that have t- ditionally been classi?ed as combinatorial because
their feasible sets are discrete. For example, the knapsack
problem's goal is to pack objects of differing weight and value so
that the knapsack's total weight is less than a given maximum and
the value of the items inside is maximized (see Sect. 2. 2. 1). The
set of feasible solutions - vectors whose components are
nonnegative integers - is both numerical and discrete. To handle
such problems while retaining full precision, Differential
Evolution copies ?oating-point - lutions to a temporary vector
that, prior to being evaluated, is truncated to the nearest
feasible solution, e. g. , by rounding the temporary parameters to
the nearest nonnegative integer.
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