Math. Model. Nat. Phenom.
Volume 13, Number 3, 2018
Modelling in Ecology, Epidemiology and Evolution
|Number of page(s)||14|
|Published online||18 October 2018|
Adaptive fitness landscape for replicator systems: to maximize or not to maximize
Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University,
Moscow 119992, Russia.
2 Applied Mathematics–1, Russian University of Transport, Moscow 127994, Russia.
3 Department of Mathematics, North Dakota State University, Fargo, ND 58108, USA.
* Corresponding author: email@example.com
Accepted: 12 March 2018
Sewall Wright’s adaptive landscape metaphor penetrates a significant part of evolutionary thinking. Supplemented with Fisher’s fundamental theorem of natural selection and Kimura’s maximum principle, it provides a unifying and intuitive representation of the evolutionary process under the influence of natural selection as the hill climbing on the surface of mean population fitness. On the other hand, it is also well known that for many more or less realistic mathematical models this picture is a severe misrepresentation of what actually occurs. Therefore, we are faced with two questions. First, it is important to identify the cases in which adaptive landscape metaphor actually holds exactly in the models, that is, to identify the conditions under which system’s dynamics coincides with the process of searching for a (local) fitness maximum. Second, even if the mean fitness is not maximized in the process of evolution, it is still important to understand the structure of the mean fitness manifold and see the implications of this structure on the system’s dynamics. Using as a basic model the classical replicator equation, in this note we attempt to answer these two questions and illustrate our results with simple well studied systems.
Mathematics Subject Classification: 92D10 / 92D15
Key words: Adaptive fitness landscape / population fitness / Fisher’s theorem of natural selection / Kimura’s maximum principle
© 2018, EDP Sciences
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.