Evolutionary Computation for Modeling and Optimization
Bek Adil · The Computer Journal · 2007
There is an increasing trend in the scientific community to model and solve complex optimization problems by employing natural metaphors. This is mainly due to the inefficiency of classical optimization algorithms in modelling and solving larger scale combinatorial and/or highly non-linear problems. It has been shown that nature-inspired, meta-heuristic algorithms can provide far better solutions than classical algorithms. The branch of nature-inspired algorithms which are known as evolutionary algorithms are focused on evolutionary processes in order to develop some meta-heuristics which can mimic natural evolutionary processes. Genetic algorithms and genetic programming are some of the well-known algorithms that mimic evolutionary processes in problem modelling and solution. The present book is mainly focused on genetic algorithms and genetic programming, and successfully explains evolutionary computation through many different applications of these algorithms. The book comprises fifteen chapters. It starts by explaining evolutionary computation through analogies from biology. Then it explains designing evolutionary...