Dynamic optimisation is an area of application where randomised search heuristics like evolutionary algorithms and artificial immune systems are often successful. The theoretical foundation of this important topic suffers from a lack of a generally accepted analytical framework as well as a lack of widely accepted example problems. This article tackles both problems by discussing necessary conditions for useful and practically relevant theoretical analysis as well as introducing a concrete family of dynamic example problems that draws inspiration from a well-known static example problem and exhibits a bi-stable dynamic. After the stage has been set this way, the framework is made concrete by presenting the results of thorough theoretical and statistical analysis for mutation-based evolutionary algorithms and artificial immune systems.
|Number of pages||29|
|Publication status||Published - 15 Dec 2015|
- dynamic optimisation problems
- evolutionary algorithms
- artificial immune systems
- fixed budget computations
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- Faculty of Business and Physcial Sciences, Department of Computer Science - Reader, Head of Department (Computer Science)
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- Faculty of Business and Physcial Sciences, Department of Computer Science - Senior Lecturer
Person: Teaching And Research