Dr George RA Hancock 🦋🦎

@grahancock.bsky.social

Postdoc zoologist interested in how ecology shapes form and function in particular colouration. Creator of the CamoEvo toolbox and professional nerd. Ghancockzoology@gmail.com

Hello, fellow evolutionary biologists. I'm someone who frequently uses Genetic Algorithms for research, see CamoEvo etc. A few times now, I've had trouble in peer review for the term "gene" being used to describe the numbers encoding the phenotypes. What is a good alternative?

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🧵 New paper! We’re excited to release OPTICS: software that immediately predicts color sensitivity from any opsin protein sequence. In other words: give OPTICS any opsin sequence, and it predicts the wavelength of light the pigment is most sensitive to 🌈👁️ academic.oup.com/mbe/advance-...

Accessible and Robust Machine Learning Approaches to Improve the Opsin Genotype-Phenotype Map

Abstract. Predicting phenotypes from genetic variation is a central challenge in biology. Here, machine learning (ML) offers great promise, but its use is

academic.oup.com

Good news! Ministers have finally given green light to new #NaturalHistory GCSE. We’ve campaigned so long for this! Big hats off to tireless advocate Mary Colwell 🙌 Now our young people will have a chance to get to know & love the natural world, to feel awe & wonder, & gain skills to protect it 🌷🌳🦡

Natural history GCSE to teach teenagers to plant wildflower-friendly gardens

Long-awaited course to examine human effects on natural world and explore everyday ways to aid biodiversity

theguardian.com

🚨 We're hiring computational evolutionary biologists! 🚨 Boosting this - industry evolutionary biology jobs don't come along often, so if you're excited to apply your computational evolutionary biology skills to cutting-edge problems, let's talk! 🧪🧬💻

Rishi De-Kayne@rishidekayne.bsky.social · 2mo ago

Our team at @arcadiascience.com is hiring! We're looking to hire a computational evolutionary biologist interested in asking how and why protein sequences/structures/functions differ across the tree of life 🧬 jobs.lever.co/arcadiascien... Please share widely!

I am pleased to announce that my submission to ALIFE 2026 has been accepted as a talk. Part of a larger collaboration on “inverse design” for multi-agent motion models, this EvoFlock side project uses a genetic algorithm to tune simulation parameters to meet a behavioral objective function. #ALife

EvoFlock: evolved inverse design of multi-agent motion

Abstract
This paper describes an automatic method for adjusting or tuning models of multi-agent motion. Simulating the motion of bird flocks, human crowds, vehicle traffic, and other multi-agent systems is a widely used technique. These simulations model the behavior of a single group member (bird, human, or vehicle). The group behaviors (flock, crowd, traffic) emerge from interactions between group members. These models typically have many numerical control parameters. Even if each parameter is intuitive in isolation, their interaction can be complex and nonlinear. It is challenging to determine which parameters to adjust for the desired change in group behavior. Changing one aspect of group behavior often causes other aspects to change, leading to a tedious process of incremental changes. In this work, the desired group behavior is measured with an objective(/fitness/loss) function and optimized with a genetic algorithm. The objective function used here for basic flocking rewards proper spacing with neighbors, flying near a desired speed, and avoiding obstacles. Interestingly, the vivid alignment seen in bird flocks appears to emerge from maintaining proper spacing between flockmates.

https://drive.google.com/file/d/1kUtEAs9WsHG-mLG4UbsIwxEEnhpsiRYY/view