The number of offspring produced by each parent---that is, the fecundity of reproducing individuals---varies among evolutionary computation methods and settings. In most prior work fecundity has been tied directly to selectivity, with higher selection pressure giving rise to higher fecundity among individuals selected to reproduce. In nature, however, there is a wider variety of strategies, with different organisms producing different numbers of offspring under the influence of a range of factors including not only selection pressure but also other factors such as environmental stability and competition within a niche. In this work we consider possible lessons that may be drawn from nature's approaches to these issues and applied to evolutionary computation systems. In particular, we consider ways in which fecundity can be dissociated from selectivity and situations in which it may be beneficial to do so. We present a simple modification to the standard evolutionary algorithm, called decimation, that permits high fecundity in conjunction with modest selection pressure and which could be used in various forms of evolutionary computation. We also present a simple example, showing that decimation can improve the problem-solving performance of a genetic algorithm when applied to a deceptive problem.
['Lee Spector', 'Thomas Helmuth', 'Kyle Ira Harrington']
Fecundity and selectivity in evolutionary computation