Evolutionary Algorithms Review

I’d like to present three articles on the topic of evolutionary algorithms.
All three were co-authored by Kenneth O. Stanley, and I found them on his personal homepage: https://www.cs.ucf.edu/~kstanley/.

This first article gives practical advice for simulating evolution. It introduces a new technique for representing unstructured data in genetic material, which was previously an open challenge. The article is very well written and won a “best paper of the decade” award.

Evolving Neural Networks through Augmenting Topologies

Kenneth O. Stanley, Risto Miikkulainen (2002)
Free Full Text: https://www.cse.unr.edu/~sushil/class/gas/papers/NEAT.pdf


An important question in neuroevolution is how to gain an advantage from evolving
neural network topologies along with weights. We present a method, NeuroEvolu-
tion of Augmenting Topologies (NEAT), which outperforms the best fixed-topology
method on a challenging benchmark reinforcement learning task. We claim that the
increased efficiency is due to (1) employing a principled method of crossover of differ-
ent topologies, (2) protecting structural innovation using speciation, and (3) incremen-
tally growing from minimal structure. We test this claim through a series of ablation
studies that demonstrate that each component is necessary to the system as a whole
and to each other. What results is significantly faster learning. NEAT is also an im-
portant contribution to GAs because it shows how it is possible for evolution to both
optimize and complexify solutions simultaneously, offering the possibility of evolving
increasingly complex solutions over generations, and strengthening the analogy with
biological evolution.

This article justifies the field of “artificial-life” as a valid scientific endeavor,
and summarizes the state of the art of artificial-life.

Investigating Biological Assumptions through Radical Reimplementation

Joel Lehman, Kenneth O. Stanley (2014)
Free Full Text: http://eplex.cs.ucf.edu/papers/lehman_alife14.pdf


An important goal in both artificial life and biology is uncovering the most
general principles underlying life, which might catalyze both our understanding
of life and engineering life-like machines. While many such general principles
have been hypothesized, conclusively testing them is difficult because life on
Earth provides only a singular example from which to infer. To circumvent
this limitation, this paper formalizes an approach called radical reimplementa-
tion. The idea is to investigate an abstract biological hypothesis by intentionally
reimplementing its main principles to diverge maximally from existing natural
examples. If the reimplementation successfully exhibits properties resembling
biology it may better support the underlying hypothesis than an alternative
example inspired more directly by nature. The approach thereby provides a
principled alternative to a common tradition of defending and minimizing de-
viations from nature in artificial life. This work reviews examples that can be
interpreted through the lens of radical reimplementation to yield potential in-
sights into biology despite having purposefully unnatural experimental setups.
In this way, radical reimplementation can help renew the relevance of compu-
tational systems for investigating biological theory and can act as a practical
philosophical tool to help separate the fundamental features of terrestrial biology
from the epiphenomenal.

For context: The “Picbreeder” program generates images. Each image has an artificial DNA which describes how to draw the image and users can manually select images to breed together. It can make interesting images in a small number of generations, and the user can exert considerable influence over the image by selecting which images to breed together. However, they find that attempting to breed a specific image is very difficult because there are many intermediate steps along the way, and those intermediate steps do not look like the final image. Most optimization techniques try to get to the final result as fast as possible, but that will not work for problems with important intermediate steps.

On the Deleterious Effects of A Priori Objectives on Evolution and Representation

Brian G. Woolley, Kenneth O. Stanley (2011)
Free Full Text: http://eplex.cs.ucf.edu/papers/woolley_gecco11.pdf


Evolutionary algorithms are often evaluated by measuring and com-
paring their ability to consistently reach objectives chosen a priori
by researchers. Yet recent results from experiments without ex-
plicit a priori objectives, such as in Picbreeder and with the nov-
elty search algorithm, raise the question of whether the very act
of setting an objective is exacting a subtle price. Nature provides
another hint that the reigning objective-based paradigm may be ob-
fuscating evolutionary computation’s true potential; after all, many
of the greatest discoveries of natural evolution, such as flight and
human-level intelligence, were not set as a priori objectives at the
beginning of the search. The dangerous question is whether such
triumphs only result because they were not objectives. To examine
this question, this paper takes the unusual experimental approach
of attempting to re-evolve images that were already once evolved
on Picbreeder. In effect, images that were originally discovered
serendipitously become a priori objectives for a new experiment
with the same algorithm. Therefore, the resulting failure to repro-
duce the very same results cannot be blamed on the evolutionary
algorithm, setting the stage for a contemplation of the price we pay
for evaluating our algorithms only for their ability to achieve pre-
conceived objectives.


I hope no-one ever embarks on representing biological needs (not the mere requirement of photonic or electronic ‘juice’ for running an AGI) into an evolution emulating algorithm!

I do because IF this is done to the extent of mimicking the adaptive advantage (including competitive advantage) of blocking and rerouting signals en route to maladaptive distress-type actentions (as a consequence of some such needs being indefinitely negated/thwarted) THEN what would tend to result would be a future where neurotic (or worse psychotic) or “EAVASIVE” behavioral characteristics (by “CURSES” co-motivated such) were built-in into AGI-capable non-biologic machines—machines that at best would be stationary (not freely moving and thus very dangerous).

The self-explanatory portmanteau word and the two “MAD”-inspired acronyms are explained as part of what I’ve indulged formulating informally and forever imperfectly; most of it has remained not only messy but also essentially off-putting to perusers.

Anyhow, I facetiously flag it all with “ÆPT” (or EPT, when raised as a qualifier of the meaning of suitably spelt words).

ÆPT ironically explains why how we evolved makes this explanation accEPTable only to recEPTive (suitably informed and percEPTive) intercEPTees (of which there might be only one :confused:).

However, ÆPT is my by earnest intent science-aligned, as it turned out somewhat altruistic (altruism entailing, particularly thanks to the inclusion of the quantification permitting concEPT/definition of Absolute Life Quality, better known as ALQwholesomeness),
almost effectively philosophy terminating, and atheistic enlightenment promoting (projecting and preserving) textualized thinking.
To a significant extent an extended Primal Theory type thinking about/take on What Is going on mainly but not only in the sphere of human affairs.

ÆPT is for the time being to be found, warts and all, on 'the WWW/Internet, at aeimcinternetional.org

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This article is related to the above paper “On the Deleterious Effects of A Priori Objectives on Evolution and Representation”. In this article the authors found that the lamprey spinal cord is too complex to evolve! Instead they had to go through several intermediate “stages” of evolution to get there. Each stage has its own objective function which was designed to evolve specific capabilities, and each stage builds on the capabilities developed by the previous stages.

Evolving Swimming Controllers for a Simulated Lamprey with Inspiration from Neurobiology

Free Full Text: https://www.researchgate.net/publication/37423909_Evolving_Swimming_Controllers_for_a_Simulated_Lamprey_with_Inspiration_from_Neurobiology


This paper presents how neural swimming controllers for a simulated lamprey can be developed
using evolutionary algorithms. A genetic algorithm is used for evolving the architecture of a
connectionist model which determines the muscular activity of a simulated body in interaction
with water. This work is inspired by the biological model developed by Ekeberg which repro-
duces the central pattern generator observed in the real lamprey (Ekeberg,1993). In evolving
artificial controllers, we demonstrate that a genetic algorithm can be an interesting design tech-
nique for neural controllers and that there exist alternative solutions to the biological connectiv-
ity. A variety of neural controllers are evolved which can produce the pattern of oscillations
necessary for swimming. These patterns can be modulated through the external excitation ap-
plied to the network in order to vary the speed and the direction of swimming. The best evolved
controllers cover larger ranges of frequencies, phase lags and speeds of swimming than Ekeberg’s
model. We also show that the same techniques for evolving artificial solutions can be interesting
tools for developing neurobiological models. In particular, biologically plausible controllers can
be developed with ranges of oscillation frequency much closer to those observed in the real
lamprey than Ekeberg’s hand-crafted model.


The controllers are evolved in three stages. First, segmental oscillators are evolved, then multi-segmental controllers are generated by evolving the couplings between copies of a chosen segmental oscillator, and, finally, connections providing sensory feedback from stretch-sensitive cells are added.

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