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Biological evolution can be conceptualized as a search process in the space of gene sequences guided by the fitness landscape, a mapping that assigns a measure of reproductive value to each genotype. Here we discuss probabilistic models of…

Populations and Evolution · Quantitative Biology 2024-04-10 Joachim Krug , Daniel Oros

A fitness landscape is a genetic space -- with two genotypes adjacent if they differ in a single locus -- and a fitness function. Evolutionary dynamics produce a flow on this landscape from lower fitness to higher; reaching equilibrium only…

Populations and Evolution · Quantitative Biology 2013-08-26 Artem Kaznatcheev

The class of epistatic fitness landscapes is much more diverse than the class of non-epistatic landscapes, and so it stands to reason that there exist dynamical phenomena that can only be realized in the presence of epistasis. Here, we…

Populations and Evolution · Quantitative Biology 2015-03-10 David M. McCandlish , Jakub Otwinowski , Joshua B. Plotkin

Local search is widely used to solve combinatorial optimisation problems and to model biological evolution, but the performance of local search algorithms on different kinds of fitness landscapes is poorly understood. Here we consider how…

Data Structures and Algorithms · Computer Science 2020-11-13 Artem Kaznatcheev , David A. Cohen , Peter G. Jeavons

The fitness landscape encodes the mapping of genotypes to fitness and provides a succinct representation of possible trajectories followed by an evolving population. Evolutionary accessibility is quantified by the existence of…

Populations and Evolution · Quantitative Biology 2021-06-30 Joachim Krug

It is still unclear how an evolutionary algorithm (EA) searches a fitness landscape, and on what fitness landscapes a particular EA will do well. The validity of the building-block hypothesis, a major tenet of traditional genetic algorithm…

Neural and Evolutionary Computing · Computer Science 2007-05-23 Theodore C. Belding

The idea of adaptive walks on fitness landscapes as a means of studying evolutionary processes on large time scales is extended to fitness landscapes that are slowly changing over time. The influence of ruggedness and of the amount of…

Biological Physics · Physics 2009-10-31 Claus O. Wilke , Thomas Martinetz

Fitness landscapes are a useful concept to study the dynamics of meta-heuristics. In the last two decades, they have been applied with success to estimate the optimization power of several types of evolutionary algorithms, including genetic…

Neural and Evolutionary Computing · Computer Science 2020-01-31 Nuno M. Rodrigues , Sara Silva , Leonardo Vanneschi

Random walks on multidimensional nonlinear landscapes are of interest in many areas of science and engineering. In particular, properties of adaptive trajectories on fitness landscapes determine population fates and thus play a central role…

Populations and Evolution · Quantitative Biology 2014-10-08 Michael Manhart , Alexandre V. Morozov

We introduce a new model of evolution on a fitness landscape possessing a tunable degree of neutrality. The model allows us to study the general properties of molecular species undergoing neutral evolution. We find that a number of…

adap-org · Physics 2007-05-23 M. E. J. Newman , Robin Engelhardt

Evolving systems, be it an antibody repertoire in the face of mutating pathogens or a microbial population exposed to varied antibiotics, constantly search for adaptive solutions in time-varying fitness landscapes. Generalists correspond to…

Biological Physics · Physics 2020-07-01 Shenshen Wang , Lei Dai

The tempo and mode of an adaptive process is strongly determined by the structure of the fitness landscape that underlies it. In order to be able to predict evolutionary outcomes (even on the short term), we must know more about the nature…

Populations and Evolution · Quantitative Biology 2013-02-28 Bjørn Østman , Christoph Adami

A common view in evolutionary biology is that mutation rates are minimised. However, studies in combinatorial optimisation and search have shown a clear advantage of using variable mutation rates as a control parameter to optimise the…

Populations and Evolution · Quantitative Biology 2019-08-27 Roman V. Belavkin , Alastair Channon , Elizabeth Aston , John Aston , Rok Krasovec , Christopher G. Knight

We propose a minimal model to simulate long waiting times followed by evolutionary bursts on rugged landscapes. It combines point and inversions-like mutations as sources of genetic variation. The inversions are intended to simulate one of…

Populations and Evolution · Quantitative Biology 2021-05-13 Leonardo Trujillo , Paul Banse , Guillaume Beslon

Epistasis occurs when the effect of a mutation depends on its carrier's genetic background. Despite increasing evidence that epistasis for fitness is common, its role during evolution is contentious. Fitness landscapes, mappings of genotype…

Populations and Evolution · Quantitative Biology 2022-06-13 Claudia Bank

Darwinian evolution can be illustrated as an uphill walk in a landscape, where the surface consists of genotypes, the height coordinates represent fitness, and each step corresponds to a point mutation. Epistasis, roughly defined as the…

Quantitative Methods · Quantitative Biology 2013-05-08 Kristina Crona

The genotype-fitness map plays a fundamental role in shaping the dynamics of evolution. However, it is difficult to directly measure a fitness landscape in practice, because the number of possible genotypes is astronomical. One approach is…

Populations and Evolution · Quantitative Biology 2014-11-11 Jakub Otwinowski , Joshua B. Plotkin

It has recently been noted that the relative prevalence of the various kinds of epistasis varies along an adaptive walk. This has been explained as a result of mean regression in NK model fitness landscapes. Here we show that this…

Populations and Evolution · Quantitative Biology 2015-06-16 Devin Greene , Kristina Crona

Evolutionary methods have long been useful for analysis and explanation in genetics, biology, ecology, and related fields. In this work, we extend these methods to neural networks, specifically large language models (LLMs), to better…

Neural and Evolutionary Computing · Computer Science 2026-05-06 Shannon K. Gallagher , Swati Rallapalli , Tyler Brooks , Chuck Loughin , Michele Sezgin , Ronald Yurko

Evolutionary algorithms offer great promise for the automatic design of robot bodies, tailoring them to specific environments or tasks. Most research is done on simplified models or virtual robots in physics simulators, which do not capture…

Robotics · Computer Science 2020-05-20 Tonnes F. Nygaard , David Howard , Kyrre Glette
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