An Empirical Study on GAs "Without Parameters"
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Investigations in meta-GAs: panaceas or pipe dreams?
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Revisiting evolutionary algorithms with on-the-fly population size adjustment
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Is self-adaptation of selection pressure and population size possible?: a case study
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Idealized dynamic population sizing for uniformly scaled problems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Meta-evolved empirical evidence of the effectiveness of dynamic parameters
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
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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, μ, all hampered by a variety of problems. This paper investigates the benefits of making μ 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.