paper-with-me

Papers

Evolutionary computational platform for the automatic discovery of nanocarriers for cancer treatment

2021-02-01 · Namid Stillman, Igor Balaz, Antisthenis Tsompanas, Marina Kovacevic, Sepinoud Azimi, Sebastien Lafond, Andrew Adamatzky, Sabine Hauert

We present the EVONANO platform for the evolution of nanomedicines with application to anti-cancer treatments. EVONANO includes a simulator to grow tumours, extract representative scenarios, and then simulate nanoparticle transport through these scenarios to predict nanoparticle distribution. The nanoparticle designs are optimised using machine learning to efficiently find the most effective anti-cancer treatments. We demonstrate our platform with two examples optimising the properties of nanoparticles and treatment to selectively kill cancer cells over a range of tumour environments.

📄 PDF Abstract BibTeX arXiv:2102.00879

Code (0)

등록된 구현이 없습니다.

Tasks

BIG-bench Machine Learning

Similar Papers 제목 키워드 기반

Evolutionary Intelligence for Scientific Discovery: From Evolutionary Computation to Cumulative Discovery Systems

2026-07-10 · Chao Wang, Lingling Li, Fang Liu, Licheng Jiao arxiv

Artificial intelligence (AI) is shifting scientific discovery from task-specific workflows towards autonomous systems that organize exploration with experimental and human feedback in open-ended candidate spaces. Evoluti…

BenchENAS: A Benchmarking Platform for Evolutionary Neural Architecture Search

2022-12-01 · IEEE Transactions on Evolutionary Computation 2022 12 · Xiangning Xie; Yuqiao Liu; Yanan Sun; Gary G. Yen; Bing Xue; Mengjie Zhang

Neural architecture search (NAS), which automatically designs the architectures of deep neural networks, has achieved breakthrough success over many applications in the past few years. Among different classes of NAS meth…

BenchmarkingGPUNeural Architecture Search

Learning Interestingness in Automated Mathematical Theory Formation

2025-11-05 · George Tsoukalas, Rahul Saha, Amitayush Thakur, Sabrina Reguyal 외 arxiv

We take two key steps in automating the open-ended discovery of new mathematical theories, a grand challenge in artificial intelligence. First, we introduce $\emph{FERMAT}$, a reinforcement learning (RL) environment that…

Reinforcement Learning

An Evolutionary Framework for Automatic and Guided Discovery of Algorithms

2019-04-05 · Ruchira Sasanka, Konstantinos Krommydas

This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) fun…

Evolutionary Algorithms

LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms

2025-05-27 · Wenhu Li, Niki van Stein, Thomas Bäck, Elena Raponi

Bayesian optimization (BO) is a powerful class of algorithms for optimizing expensive black-box functions, but designing effective BO algorithms remains a manual, expertise-driven task. Recent advancements in Large Langu…

Bayesian OptimizationBenchmarkingLanguage ModelingLanguage Modelling+2