Evolution may change speed, but it does not make big jumps (at least very often). It typically progresses through numerous small moves, exploring adjacent spaces of what else might be possible. Some of those spaces lead to better fitness, some to less. This is low cost exploration, big mutational jumps involve much more risk that the changes will be dysfunctional, or even terminal in the immediate short term.
The same strategy may apply to many development programs, where big changes may require a very different set of human capital, whereas incremental changes would only require small changes in human capital requirements
[But for a counter view, read about “hopeful monsters“]
Incremental searches for small improvements in a predictive model can be made in two ways:
- Testing the addition of new attributes, one at a time. This is now possible with EvalC3 by manually setting up the attributes of a design and then clicking on Find New Models and selecting “Find one additional attribute that gives best performance” This will enlarge the current model by one attribute. Repeating this process will expand the size of the current model by one attribute at a time
- Testing the effects of the removal of existing attributes of the design, one at a time. This has to be done manually, in the Design menu. For small sized models this should not be too time consuming. It is in effect a form of Sensitivity Analysis
For more on the idea of “the adjacent possible” see:
Spaces of the possible: universal Darwinism and the wall between technological and biological innovation. Andreas Wagner, William Rosen. 2014
Kauffman, Stuart A. Investigations. Oxford University Press,
Johnson, Steven. Where Good Ideas Come From: The Seven Patterns of Innovation.Where Good Ideas Come From: The Seven Patterns of Innovation. London: Penguin, 2011.
The adjacent possible. 2010. Eddie Smith’s Practically Effecient blog
“The Atlas of Economic Complexity.” MIT Press. Accessed January 12, 2016. https://mitpress.mit.edu/books/atlas-economic-complexity.