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5
Evolutionary Computation
5.1 Introduction
The study of evolutionary behaviour of biological processes has produced a
constructive impact on development of a new intelligent computational approach
for solving complex optimization problems in mathematics, natural sciences,
engineering and in real-life in general. The algorithms developed under the
common term of evolutionary computation are mainly based on selection of a
population as a possible initial solution of a given problem. Through stepwise
processing of initial population using evolutionary operators , such as crossover ,
recombination , selection , and mutation , the fitness of the initial population
steadily improves. Following this evolutionary concept, various computational
algorithms have been elaborated, such as genetic algorithms , genetic
programming , evolutionary strategies , and evolutionary programming , that are
capable of solving complex problems, where the traditional mathematical methods
cannot be applied easily. Depending on the nature of the problem in hand, the most
adequate algorithm is to be selected.
The primary application area of evolutionary algorithms that we are interested
in concerns the forecasting of time series data, but also evolving neural networks
and fuzzy logic systems. It will be shown that the synergetic effects of
combinations of different computational technologies - the neural networks, fuzzy
logic, and evolutionary computation - help in designing the improved intelligent
systems and also help in improving the accuracy and the convergence speed of
evolutionary algorithms themselves. This will be discussed in Part 3 of the topic,
which is dedicated to the hybrid computational technologies.
It is well known that evolutionary computation is a category of algorithms,
based on Darwin's idea of evolution of living creatures. According to this idea,
every living creature has a single predecessor that has to adapt steadily to the
changing environment in the attempt to survive. According to Darwin, the idea of
adaptation is strongly connected with the principle of natural selection, because the
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