Genetic algorithm behaviour in the solution of an inverse thermal field problem

Discusses the performance of a simple genetic algorithm (GA) applied to a one‐dimensional inverse thermal field problem. Builds on these results by considering changes in GA performance that result from the introduction of non‐complementary crossover, stochastic remainder sampling and a combination of the two. Shows that, in comparison to the simple GA, non‐complementary cross‐over provides more rapid convergence, while stochastic remainder sampling without replacement has the opposite effect. However, when both strategies are combined, they provide considerably better performance with greater diversity within the population.