18 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…
Gang Liu, Yihan Zhu, Jie Chen, Meng Jiang
Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in…
Carlos Contreras Bolton, Gustavo Gatica, Víctor Parada, Aaron Alain-Jon Golden
'Aaron Alain-Jon Golden'] The vertex coloring problem is a classical problem in combinatorial optimization that consists of assigning a color to each vertex of a graph such that no adjacent vertices share the same color, minimizing the number of colors used. Despite the various practical applications that exist for…
Jean-Baptiste Mouret
Evolution gave rise to creatures that are arguably more sophisticated than the greatest human-designed systems. This feat has inspired computer scientists since the advent of computing and led to optimization tools that can evolve complex neural networks for machines—an approach known as ''neuroevolution.'' After a few…
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…
Roman V. Yampolskiy
In this paper, we review the state-of-the-art results in evolutionary computation and observe that we don't evolve non-trivial software from scratch and with no human intervention. A number of possible explanations are considered, but we conclude that computational complexity of the problem prevents it from being…
Alexander Novikov, Nhat Vu, Eisenberger, Marvin + 15 more
Alexander Novikov \ , Ngân Vu˜ \ , Marvin Eisenberger \ , Emilien Dupont \ , Po-Sen Huang \ , Adam Zsolt Wagner \ , Sergey Shirobokov \ , Borislav Kozlovskii \ , Francisco J. R. Ruiz, Abbas Mehrabian, M. Pawan Kumar, Abigail See, Swarat Chaudhuri, George Holland, Alex Davies, Sebastian Nowozin, Pushmeet Kohli and Matej…
Nicola Milano, Paolo Pagliuca, Stefano Nolfi
We show how the characteristics of the evolutionary algorithm influence the evolvability of candidate solutions, i.e. the propensity of evolving individuals to generate better solutions as a result of genetic variation. More specifically, (1+λ) evolutionary strategies largely outperform (μ+1) evolutionary strategies in…
Iztok Fister, Iztok Fister
The main deficiency of the algorithms running on digital computers nowadays is their inability to change themselves during the execution. In line with this, the paper introduces the so-called replicated algorithms, inspired by the concept of developing a human brain. Similar to the human brain, where the process of…
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.…
Valdimir Pieter
In this paper, the sources is explicitly stated in the text or the footnotes. Moreover, all code used within this paper were designed and implemented from scratch. The goal of this paper is to provide the readers with information that will give them a better understanding on the different types of algorithm and the…
Sizhe Yuen, Thomas H. G. Ezard, Adam J. Sobey
Evolutionary computation is a group of biologically inspired algorithms used to solve complex optimization problems. It can be split into evolutionary algorithms, which take inspiration from genetic inheritance, and swarm intelligence algorithms, that take inspiration from cultural inheritance. However, much of the…
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…
Rosangela Canino-Koning, Michael J. Wiser, Charles Ofria, Laurent Keller
'Laurent Keller'] Genetic spaces are often described in terms of fitness landscapes or genotype-to-phenotype maps, where each genetic sequence is associated with phenotypic properties and linked to other genotypes that are a single mutational step away. The positions close to a genotype make up its “mutational…
Alexander V. Spirov, Ekaterina M. Myasnikova, Josh Bongard
Evolutionary computing (EC) is an area of computer sciences and applied mathematics covering heuristic optimization algorithms inspired by evolution in Nature. EC extensively study all the variety of methods which were originally based on the principles of selectionism. As a result, many new algorithms and approaches…
Diego Navarro-Mateu, Ana Cocho-Bermejo
The majority of current visual-algorithmic architecture is constricted to specific parameters that are gradient related, keeping their parts’ relation fixed within the algorithm, far away from a truly parametric modeling with a flexible topology. Recent findings around genetics and certain genes capable of shape…
Oliver Attie, Brian Sulkow, Chong Di, Weigang Qiu + 1 more
The Standard Genetic Code (SGC) is robust to mutational errors such that frequently occurring mutations minimally alter the physio-chemistry of amino acids. The apparent correlation between the evolutionary distances among codons and the physio-chemical distances among their cognate amino acids suggests an early…
Michael Tetteh, Douglas Mota Dias, Conor Ryan
The evolution of complex circuits remains a challenge for the Evolvable Hardware field in spite much effort. There are two major issues: the amount of testing required and the low evolvability of representation structures to handle complex circuitry, at least partially due to the destructive effects of genetic…