The effects of selection on noisy fitness optimization
Sergiu Goschin, Michael L. Littman, David H. Ackley · 2011
This paper examines how the choice of the selection mechanism in an evolutionary algorithm impacts the objective function it optimizes, specifically when the fitness function is noisy. We provide formal results showing that, in an abstract infinite-population model, proportional selection optimizes expected fitness, truncation selection optimizes order statistics, and tournament selection can oscillate. The "winner" in a population depends on the choice of selection rule, especially when fitness distributions differ between individuals resulting in variable risk. These findings are further developed through empirical results on a novel stochastic optimization problem called "Die4", which, while simple, extends existing benchmark problems by admitting a variety of interpretations of optimality.