12 papers · ranked by Valyu relevance
Zhimeng Zhou, Yang Nan, Minjie Mou, Yuntao Qian + 16 more
Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug…
Artem Kaznatcheev
Valiant [1] proposed to treat Darwinian evolution as a special kind of computational learning from statistical queries. The statistical queries represent a genotype’s fitness over a distribution of challenges. And this distribution of challenges along with the best response to them specify a given abiotic environment…
Stefano Nolfi, Paolo Pagliuca
We investigate the use of competitive co-evolution for synthesizing progressively better solutions. Specifically, we introduce a set of methods to measure historical and global progress. We discuss the factors that facilitate genuine progress. Finally, we compare the efficacy of four qualitatively different algorithms.…
Nhat Khang Ngo, Thanh V. T. Tran, Viet Thanh Duy Nguyen, Truong Son Hy
Directed evolution has been the most effective method for protein engineering that optimizes biological functionalities through a resource-intensive process of screening or selecting among a vast range of mutations. To mitigate this extensive procedure, recent advancements in machine learning-guided methodologies…
Martín Gutiérrez, Yerko Ortiz, Javier Carrión
Metaheuristic procedures (MH) have been a trend driving Artificial Intelligence (AI) researchers for the past 50 years. A variety of tools and applications (not only in Computer Science) stem from these techniques. Also, MH frequently rely on evolution, a trademark process involved in cell colony growth. Generally, MH…
Lillian T. Tatka, Lucian P. Smith, Herbert M. Sauro
Evolutionary algorithms, a class of optimization techniques inspired by biological evolution, have emerged as powerful tools for the optimization of complex systems, including the evolution of mass-action chemical reaction networks. This work explores the application of evolutionary algorithms in this domain…
Dániel Czégel, Hamza Giaffar, Márton Csillag, Bálint Futó + 1 more
Efficient search in enormous combinatorial spaces is an essential component of intelligence. Humans, for instance, are often found searching for optimal action sequences, linguistic structures and causal explanations. Is there any computational domain that provides good-enough and fast-enough solutions to such a…
S. Sudha, S. Baskar, S. Krishnaswamy
The objective of protein docking is to achieve a relative orientation and an optimized conformation between two proteins that results in a stable structure with the minimized potential energy. Constrained Self-adaptive Differential Evolution (Cons_SaDE) algorithm is used to find the minimum energy conformation using…
Oliver Attie, Brian Sulkow, Chong Di, Wei-Gang Qiu
Learning algorithms have been proposed as a non-selective mechanism capable of creating complex adaptive systems in life. Evolutionary learning however has not been demonstrated to be a plausible cause for the origin of a specific molecular system. Here we show that genetic codes as optimal as the Standard Genetic Code…
Ashley I. Teufel, Claus O. Wilke
We present an accelerated algorithm to forward-simulate origin--fixation models. Our algorithm requires on average only about two fitness evaluations per fixed mutation, whereas traditional algorithms require, per one fixed mutation, a number of fitness evaluations on the order of the effective population size Ne. Our…
John-Antonio Argyriadis, Yang-Hui He, Vishnu Jejjala, Djordje Minic
We study the dynamics of genetic code evolution. The algorithm of Vetsigian et al. [1] provides a solution that is both optimal and universal. We reproduce and analyze the algorithm as a dynamical system. All the parameters used in the model are varied to assess their impact on achieving universality. We show that by…
Kiri Choi, Joesph Hellerstein, H. Steven Wiley, Herbert M. Sauro
In this paper we examine the use of perturbation data to infer the underlying mechanistic dynamic model. The approach uses an evolutionary strategy to evolve networks based on a fitness criterion that measures the difference between the experimentally determined set of perturbation data and proposed mechanistic models.…