25 papers · ranked by Valyu relevance
Inès Benito, Johannes F. Lutzeyer, Benjamin Doerr
Baldwinian and Lamarckian evolution have existed for a long time in evolutionary algorithms (EAs) without ever dominating the academic literature or practical applications. In this work, we use modern empirical and theoretical methods to revisit Lamarckian and Baldwinian evolution and rigorously compare them with the…
Csenge Petak, Lapo Frati, Renske M. A. Vroomans, Melissa H. Pespeni + 1 more
Title: Significance The speed and direction of evolution depend on the availability of phenotypic variation. Genotype-to-phenotype maps can, over time, bias phenotypic variability to more readily produce alternative adaptive phenotypes in fluctuating environments. However, because properties of the fitness landscapes…
Innocent Sibanda, Geoff Nitschke
The goal of bioengineering in synthetic biology is to redesign, reprogram, and rewire biological systems for specific applications using standardized parts such as promoters and ribosomes. For example, bioengineered micro-organisms capable of cleaning up environmental pollution or producing antibodies de novo to defend…
Alireza Rezaee
This paper presents a genetic algorithm (GA) approach to cost-optimal task scheduling in a production line. The system consists of a set of serial processing tasks, each with a given duration, unit execution cost, and precedence constraints, which must be assigned to an unlimited number of stations subject to a…
Oguz Emrah Turgut, Hadi Genceli, Mustafa Asker, Ehsan Baniasadi + 1 more
This research proposes a novel hybrid metaheuristic optimization framework that combines the Aquila Optimization algorithm with the Sine-Cosine Optimizer to find equilibrium points of reacting components under specified operational reaction conditions. The method aims to address the exploitative limitations of the…
Elisabeth Tan, Sophie C. Frechen, Bianca Broske, Julia M. Messmer + 16 more
Recent advances in AI-based structural biology have made de novo protein binder design increasingly effective, yet performance remains highly target dependent. For instance, the cancer surface antigen Nectin-4, an immunoglobulin-like cell adhesion protein, proved particularly challenging for RFdiffusion-based…
Authors not listed
Finding the most stable adsorption geometry of a flexible molecule on a catalytic surface remains a key challenge due to the high dimensionality and ruggedness of the potential energy surface. We present a Gradient-Enhanced Genetic Algorithm (GE-GA) for the global optimization of adsorbate–surface configurations…
Jin Zhu, Bojun Liu, Jun Zheng, Shaojie Yin + 2 more
The Secretary Bird Optimization Algorithm (SBOA) is a novel swarm-based meta-heuristic that formulates an optimization model by mimicking the secretary bird’s hunting and predator-evasion behaviors, and thus possesses appreciable application potential. Nevertheless, it suffers from an unbalanced…
Duc-Cuong Dang, Per Kristian Lehre
While some common fitness landscape characteristics are critical when determining the runtime of evolutionary algorithms (EAs), the relationship between fitness landscape structure and the runtime of EAs is poorly understood. Recently, Dang, Eremeev, and Lehre introduced a classification of pseudo-Boolean problems…
Mohamed Elhosseny, Mahmoud Abdel-Salam, Anand Nayyar, Emre Çelik + 3 more
The Dung Beetle Optimization (DBO) algorithm is a relatively recent metaheuristic known for its simplicity, versatility, and low parameter dependence, making it a valuable tool for solving complex optimization problems. Despite its potential, DBO suffers from limitations such as slow convergence and premature…
Maxim Sakharov, Heming Jia
Memetic algorithms achieve strong optimization performance by combining population-based global search with local refinement operators, yet their effectiveness critically depends on the design and management of memes. Local search strategies are typically handcrafted, problem-specific, and fixed prior to execution.…
João Sartori, Eduardo Krempser, Ana Carolina Ramos Guimarães, Lucas de Almeida Machado
The optimization of protein sequences for enhanced binding and stability remains a formidable challenge in bioengineering due to the vastness of sequence space. Existing state-of-the-art methods, including traditional structure-based design and protein language models, use fitness estimators as objective functions to…
Authors not listed
Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a…
Stephan Lukasczyk, Gordon Fraser
Search-based test-generation algorithms have countless configuration options. Users rarely adjust these options and usually stick to the default values, which may not lead to the best possible results. Tuning an algorithm's hyperparameters is a method to find better hyperparameter values, but it typically comes with a…
Furong Ye, Frank Neumann, Thomas Bäck, Niki van Stein
We present a novel approach for constructing discrete optimization benchmarks that enables fine-grained control over problem properties, and such benchmarks can facilitate analyzing discrete algorithm behaviors. We build benchmark problems based on a set of block functions, where each block function maps a subset of…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Minghao Yan, Bo Peng, Benjamin Coleman, Ziqi Chen + 10 more
Large language models have become drivers of evolutionary search, but most systems rely on a fixed, prompt-elicited policy to sample next candidates. This limits adaptation in practical engineering and research tasks, where evaluations are expensive, and progress depends on learning task-specific search dynamics. We…
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…
Kangbien Park
Humans have long employed directed evolution (DE) to engineer desired biological traits. In this paper, I introduce an algebraic framework that provides a quantitative representation of the general phenotypic traits of asexual populations, enabling the systematic modeling of DE processes. Within this framework, key…
Authors not listed
Compositionally complex materials, e.g. high-entropy alloys/oxides, have long been of research interest for their unique mechanical and functional properties thanks to the synergistic effects between the large number of components. More recently, they have emerged as a promising platform for the development of novel…
Liudmyla Vasylenko, Adi Livnat
At the fundamental conceptual level, two alternatives have traditionally been considered for how mutations arise and how evolution happens: 1) random mutation and natural selection, and 2) Lamarckism. Recently, the theory of Interaction-based Evolution (IBE) has been proposed, according to which mutations are neither…
Manuel Corpas
Large language model (LLM) agents are typically deployed as clones: identical copies of a single configuration with no mechanism for heritable variation or population-level dynamics. Here we introduce Genomebook, a designed evolutionary system that encodes 26 behavioural traits across 60 diploid loci using additive…
Jiayi Zhang, Yongfeng Gu, Jianhao Ruan, Maojia Song + 9 more
Agentic evolution has emerged as a powerful paradigm for improving programs, workflows, and scientific solutions by iteratively generating candidates, evaluating them, and using feedback to guide future search. However, existing methods are typically instantiated either as fixed hand-designed procedures that are…
Authors not listed
Molecular generation for pharmaceutical and agrochemical development remains computationally expensive and statistically under-validated. We conducted a comprehensive benchmark of five molecular generation methods—Genetic Algorithm, Proximal Policy Optimization (PPO), REINFORCE, Transformer-based generation, and random…
Zeki Doruk Erden
Sexual selection is among the most pervasive and costly features of biological reproduction, reaching its greatest elaboration in lineages of greater complexity and capability—costs that signal a structural function not adequately accounted for by only the existing explanations focused on immediate adaptive benefits. I…