Traditional evolutionary algorithms are powerful problem solvers that have several fixed parameters which require prior specification. Determining good values for any of these parameters can be difficult, as these parameters are generally very sensitive, requiring expert knowledge to set optimally without extensive use of trial and error. Parameter control is a promising approach to achieving this automation and has the added potential of increasing EA performance based on both theoretical and empirical evidence that the optimal values of EA strategy parameters change during the course of executing an evolutionary run. While many methods of parameter control have been published that focus on removing the population size parameter, Î¼, all hampered by a variety of problems. This paper investigates the benefits of making Î¼ a dynamic parameter and introduces two novel methods for population control. These methods are then compared to state-of-the-art population sizing EAs, exploring the strengths and weaknesses of each.
- Evolutionary Algorithm,
- Parameter Control,
- Parameterless Evolutionary Algorithm,
- Population Sizing
Available at: http://works.bepress.com/daniel-tauritz/13/