Biological adaptation by mutation, natural selection and random diffusion.
Analogies and disanalogies between tooth-and-claw natural selection, and our statistical learning methods. Stochastic process models of gene and population dynamics.
There are some notes about the artificial use of algorithms modeled on evolution under genetic algorithms.
I would like to better understand:
The replicator equation
The-no-free lunch theorem, and the co-evolutionary exception
The opposite of evolution-as-optimisation: The neutral model
Evolutionary game theory in general
- How about RNA games?
This “robustnesss and evolvability” business.
Connection with mathematical optimisation
Nisheeth Vishnoi, Nature, Dynamical Systems and Optimization
The language of dynamical systems is the preferred choice of scientists to model a wide variety of phenomena in nature. The reason is that, often, it is easy to locally observe or understand what happens to a system in one time-step. Could we then piece this local information together to make deductions about the global behavior of these dynamical systems? The hope is to understand some of nature’s algorithms and, in this quest, unveil new algorithmic techniques. In this first of a series of posts, we give a gentle introduction to dynamical systems and explain what it means to view them from the point of view of optimization.
In this post, we will see the main technical ideas in the analysis of the mixing time of evolutionary Markov chains introduced in a previous post. We start by introducing the notion of the expected motion of a stochastic process or a Markov chain. In the case of a finite population evolutionary Markov chain, the expected motion turns out to be a dynamical system which corresponds to the infinite population evolutionary dynamics with the same parameters. Surprisingly, we show that the limit sets of this dynamical system govern the mixing time of the Markov chain. In particular, if the underlying dynamical system has a unique stable fixed point (as in asexual evolution), then the mixing is fast and in the case of multiple stable fixed points (as in sexual evolution), the mixing is slow. Our viewpoint connects evolutionary Markov chains, nature’s algorithms, with stochastic descent methods, popular in machine learning and optimization, and the readers interested in the latter might benefit from our techniques.
Extended Evolutionary Synthesis
Where the memes went.
The extended evolutionary synthesis (EES) is new a way to think about and understand evolutionary phenomena that differs from the conception that has dominated evolutionary thinking since the 1930s (i.e., the modern synthesis). The EES does not replace traditional thinking, but rather can be deployed alongside it to stimulate research in evolutionary biology.
The extended evolutionary synthesis emphasizes two key unifying concepts that feature in progressive readings of some sections of the evolutionary biology literature – constructive development and reciprocal causation
- Scott Page has the friendliest explanation of the replicator equation that I’ve seen.
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