https://www.nature.com/articles/s42256-020-00278-8
Abstract
Evolutionary computation is inspired by the mechanisms of biological evolution. With algorithmic improvements and increasing computing
resources, evolutionary computation has discovered creative and
innovative solutions to challenging practical problems. This paper
evaluates how today’s evolutionary computation compares to biological evolution and how it may fall short. A small number of well-accepted characteristics of biological evolution are considered: openendedness,
major transitions in organizational structure, neutrality and genetic
drift, multi-objectivity, complex genotype-to-phenotype mappings and co-evolution. Evolutionary computation exhibits many of these to some
extent but more can be achieved by scaling up with available computing
and by emulating biology more carefully. In particular, evolutionary computation diverges from biological evolution in three key respects: it
is based on small populations and strong selection; it typically uses
direct genotype-to-phenotype mappings; and it does not achieve major organizational transitions. These shortcomings suggest a roadmap for
future evolutionary computation research, and point to gaps in our understanding of how biology discovers major transitions. Advances in
these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology, and can serve as an executable
model of biological processes.
https://www.nature.com/articles/s42256-020-00278-8
Abstract
Evolutionary computation is inspired by the mechanisms of biological evolution. With algorithmic improvements and increasing computing
resources, evolutionary computation has discovered creative and
innovative solutions to challenging practical problems. This paper
evaluates how today’s evolutionary computation compares to biological evolution and how it may fall short. A small number of well-accepted characteristics of biological evolution are considered: openendedness,
major transitions in organizational structure, neutrality and genetic
drift, multi-objectivity, complex genotype-to-phenotype mappings and co-evolution. Evolutionary computation exhibits many of these to some
extent but more can be achieved by scaling up with available computing
and by emulating biology more carefully. In particular, evolutionary computation diverges from biological evolution in three key respects: it
is based on small populations and strong selection; it typically uses
direct genotype-to-phenotype mappings; and it does not achieve major organizational transitions. These shortcomings suggest a roadmap for
future evolutionary computation research, and point to gaps in our understanding of how biology discovers major transitions. Advances in
these areas can lead to evolutionary computation that approaches the complexity and flexibility of biology, and can serve as an executable
model of biological processes.
This does not seem very relevant to paleontology or even to systematics.
On 4/13/23 08:51, John Harshman wrote:
This does not seem very relevant to paleontology or even to systematics.
You have a bit to learn. There is no such thing as systematics. There
is only computational biology and biologial statistics.
On 4/13/23 8:35 PM, Popping Mad wrote:
On 4/13/23 08:51, John Harshman wrote:
This does not seem very relevant to paleontology or even to systematics.
You have a bit to learn. There is no such thing as systematics. There
is only computational biology and biologial statistics.
!
I share John's skepticism. This article has very little connection to anything. "Let's
let AI do it!" is (at least to me) not a promising path to understanding. Even Elon Musk(!)
suggests we ought to calm down a little. Artificial intelligence and artificial stupidity are
still close cousins.
John Harshman <john.harshman@gmail.com> wrote:
On 4/13/23 8:35 PM, Popping Mad wrote:
On 4/13/23 08:51, John Harshman wrote:
This does not seem very relevant to paleontology or even to systematics. >>>
You have a bit to learn. There is no such thing as systematics. There
is only computational biology and biologial statistics.
!
Don't be suprised. I don't know what this resistence is in the Paleo community to hard math and Balesyain statistics.
All evolutionary theory can be reduced to mathmatical alorithms with
maps. I sort of giggled when I read current commentary on AI
applications. There is a failure to understand what AI does and where
the current state of the art is.
The future of Paleo is just like it is for the rest of Biology, it is computational mathmatics. The same allgorthism used to describe
econological problems and microbiology are also applicable to
evolutionary problems and Paleontology.
This is especially true as most geneitics is available.
There is no such resistance. But your citation isn't relevant to paleontology.
On 4/14/23 17:47, John Harshman wrote:
There is no such resistance. But your citation isn't relevant to
paleontology.
OK perhaps I made an error
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