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Why is optimization difficult?

By Thomas Weise, Michael W. Zapf, Raymond Chiong and Antonio J. Nebro


This chapter aims to address some of the fundamental issues that are often encountered in optimization problems, making them difficult to solve. These issues include premature convergence, ruggedness, causality, deceptiveness, neutrality, epistasis, robustness, overfitting, oversimplification, multi-objectivity, dynamic fitness, the No Free Lunch Theorem, etc. We explain why these issues make optimization problems hard to solve and present some possible countermeasures for dealing with them. By doing this, we hope to help both practitioners and fellow researchers to create more efficient optimization applications and novel algorithms

Topics: Algorithms, Engineering, Optimization
Publisher: Springer
Year: 2009
DOI identifier: 10.1007/978-3-642-00267-0_1
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