Genetic programming: optimal population sizes for varying complexity problems

The population size in evolutionary computation is a significant parameter affecting computational effort and the ability to successfully evolve solutions. We find that population size sensitivity how much a genetic program's efficiency varies with population size is correlated with problem complexity. An analysis of population sizes was conducted using a unimodal, bimodal and a multi-modal problem with varying levels of difficulty. Specifically we show that a unimodal and bimodal and multimodal problems exhibit an increased sensitivity to population size with increasing levels of difficulty. We demonstrate that as problem complexity increases, determination of the optimal population size becomes more difficult. Conversely, the less complex a problem is the more sensitive the genetic program's efficiency is to population size. Categories and Subject Descriptors I.2.2 [Artificial Intelligence]: Automatic Programming, program synthesis. General Terms Algorithms, Performance, E...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Alan Piszcz, Terence Soule
• Keywords: GECCO 2006, Genetic Program Efficiency, Optimization, Population Size, Population Size Sensitivity