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Janez Brest (University of Maribor, Slovenia)

DOI: 10.4018/978-1-59904-849-9.ch074

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TopDE creates new candidate solutions by combining the parent individual and several other individuals of the same population. A candidate replaces the parent only if it has better fitness value.

The population of the original DE algorithm (Storn & Price, 1995) (Storn & Price, 1997) contains *NP D*-dimensional vectors: **x*** _{i,G}*,

Mutant vector **v*** _{i,G}* can be created by using one of the mutation strategies (Price et al., 2005). The most useful strategy is ‘rand/1’:

After mutation, a ‘binary’ crossover operation forms the trial vector **u*** _{i,G}*, according to the

The selection operation selects, according to the objective fitness value of the population vector **x*** _{i,G}* and its corresponding trial vector

The original DE has more strategies and Feoktistov (Feoktistov, 2006) proposed some general extensions to DE strategies. The question is which strategy is the most suitable to solve a particular problem. Recently some researchers used various combinations of two, three or even more strategies during the evolutionary process.

Area of the Search Space: Set of specific ranges or values of the input variables that constitute a subset of the search space.

Control Parameter: Control parameter determines behaviour of evolutionary program (e.g. population size).

Search Space: Set of all possible situations of the optimization problem that we want to solve.

Individual: An individual represents a candidate solution. During the optimization process an evolutionary algorithm usually uses a population of individuals to solve a particular problem.

Differential Evolution: An evolutionary algorithm for global optimization, which realized the evolution of a population of individuals in a manner that uses of differences between individuals.

Evolutionary Computation: Solution approach guided by biological evolution, which begins with potential solution models, then iteratively applies algorithms to find the fittest models from the set to serve as inputs to the next iteration, ultimately leading to a model that best represents the data.

Self-Adaptation: The ability that allows an evolutionary algorithm to adapt itself to any general class of problems, by reconfiguring itself accordingly, and do this without and user interaction.

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