More than a decade after the first extensive overview on parameter control, we revisit the
field and present a survey of the state-of-the-art. We briefly summarize the development of …

Evolutionary algorithms (EAs) are robust stochastic optimisers that perform well over a wide
range of problems. Their robustness, however, may be affected by several adjustable …

The issue of setting the values of various parameters of an evolutionary algorithm is crucial
for good performance. In this paper we discuss how to do this, beginning with the issue of …

Genetic programming has highlighted the problem of bloat, the uncontrolled growth of the
average size of an individual in the population. The most common approach to dealing with …

Several studies have applied genetic programming (GP) to the task of forecasting with
favorable results. However, these studies, like those applying other techniques, have …

Bloat can be defined as an excess of code growth without a corresponding improvement in
fitness. This problem has been one of the most intensively studied subjects since the …

With the exception of a small body of adaptive-parameter literature, evolutionary
computation has traditionally favored keeping the population size constant through the …

During the evolution of solutions using genetic programming (GP) there is generally an
increase in average tree size without a corresponding increase in fitness-a phenomenon …

This paper presents a new way of measuring complexity in variable-size-chromosome-
based evolutionary algorithms. Dealing with complexity is particularly useful when …

Genetic programming (GP) and its variants have been extensively applied for modeling of
the stock markets. To improve the generalization ability of the model, GP have been …