March 2009
Intermediate to advanced
476 pages
14h 28m
English
book
Optimization Techniques for Solving Complex Problems
by Enrique Alba, Christian Blum, Pedro Asasi, Coromoto Leon, Juan Antonio Gomez
Content preview from Optimization Techniques for Solving Complex Problems
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CHAPTER 3
Optimization Using Genetic Algorithms with Micropopulations
Y. SÁEZ
Universidad Carlos III de Madrid, Spain
3.1 INTRODUCTION
Complex problems can be encountered in real-life domains such as finance and economics, telecommunications, and industrial environments. Solving these problems with evolutionary computation (EC) techniques requires deciding on what the best representations, operators, evaluation function, and parameters are. Furthermore, some complex problems need long computation times to evaluate the solutions proposed by the technique applied or even interaction with an expert. In those cases, it is necessary to decrease the number of evaluations made. One approach commonly used with population-based approaches is to reduce the number of individuals in the population. Micropopulations, which means at most 10 individuals, reduce the genetic diversity dramatically, diminishing the overall performance of the technique applied. However, there are some alternatives to addressing this problem, such as a fitness prediction genetic algorithm (FPGA) or the chromosome appearance probability matrix (CAPM) algorithm.
3.1.1 Solving Complex Problems with Micropopulations
EC encompasses computational models that follow a biological evolution metaphor. The success of these techniques is based on the maintenance of genetic diversity, for which it is necessary to work with large ...
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