Search Swinburne Research Bank
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/56238
- Why is optimization difficult?
- Weise, Thomas; Zapf, Michael W.; Chiong, Raymond; Nebro, Antonio J.
- 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.
- Publication type
- Book chapter
- Research centre
- Swinburne University of Technology. Sarawak Campus. School of Computing and Design
- Nature-inspired algorithms for optimisation / Raymond Chiong (ed.), pp. 1-50
- Publication year
- Algorithms; Engineering; Optimization
- 1860-949X (series ISSN)
- Publisher URL
- Copyright © 2009 Springer-Verlag Berlin Heidelberg.
- Peer reviewed