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Reconstructing ancestral gene orders is an important step towards understanding genome evolution. The Small Parsimony Problem (SPP) has been extensively studied in this regard. The problem aims at finding the gene orders at internal nodes…

Data Structures and Algorithms · Computer Science 2021-08-11 Daniel Doerr , Cedric Chauve

In the small phylogeny problem we, are given a phylogenetic tree and gene orders of the extant species and our goal is to reconstruct all of the ancestral genomes so that the number of evolutionary operations is minimized. Algorithms for…

Populations and Evolution · Quantitative Biology 2015-03-17 Jakub Kováč , Broňa Brejová , Tomáš Vinař

The availability of a large number of assembled genomes opens the way to study the evolution of syntenic character within a phylogenetic context. The DeCo algorithm, recently introduced by B{\'e}rard et al. allows the computation of…

Quantitative Methods · Quantitative Biology 2015-03-20 Ashok Rajaraman , Cedric Chauve , Yann Ponty

Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can still achieve good predictive performance. The problem of…

The Single Cut or Join (SCJ) operation on genomes, generalizing chromosome evolution by fusions and fissions, is the computationally simplest known model of genome rearrangement. While most genome rearrangement problems are already hard…

Computational Engineering, Finance, and Science · Computer Science 2013-04-09 Istvan Miklos , Sandor Z. Kiss , Eric Tannier

Evolution is a process that is influenced by various environmental factors, e.g. the interactions between different species, genes, and biogeographical properties. Hence, it is interesting to study the combined evolutionary history of…

Quantitative Methods · Quantitative Biology 2013-08-02 Nicolas Wieseke , Matthias Bernt , Martin Middendorf

Evolutionary computation methods have been successfully applied to neural networks since two decades ago, while those methods cannot scale well to the modern deep neural networks due to the complicated architectures and large quantities of…

Neural and Evolutionary Computing · Computer Science 2019-03-12 Yanan Sun , Bing Xue , Mengjie Zhang , Gary G. Yen

We demonstrate how a genetic algorithm solves the problem of minimizing the resources used for network coding, subject to a throughput constraint, in a multicast scenario. A genetic algorithm avoids the computational complexity that makes…

Neural and Evolutionary Computing · Computer Science 2007-05-23 Minkyu Kim , Varun Aggarwal , Una-May O'Reilly , Muriel Medard , Wonsik Kim

The chance-constrained knapsack problem is a variant of the classical knapsack problem where each item has a weight distribution instead of a deterministic weight. The objective is to maximize the total profit of the selected items under…

Neural and Evolutionary Computing · Computer Science 2020-04-09 Yue Xie , Aneta Neumann , Frank Neumann

The ancestral sequence reconstruction problem is the inference, back in time, of the properties of common sequence ancestors from measured properties of contemporary populations. Standard algorithms for this problem assume independent…

Disordered Systems and Neural Networks · Physics 2022-02-09 Edwin Rodríguez Horta , Alejandro Lage-Castellanos , Roberto Mulet

The ancestral maximum-likelihood and phylogeography problems are two fundamental problems involving evolutionary studies. The ancestral maximum-likelihood problem involves identifying a rooted tree alongside internal node sequences that…

Data Structures and Algorithms · Computer Science 2023-08-15 Mohammad-Hadi Foroughmand-Araabi , Sama Goliaei , Kasra Alishahi

Chance constrained optimization problems allow to model problems where constraints involving stochastic components should only be violated with a small probability. Evolutionary algorithms have been applied to this scenario and shown to…

Neural and Evolutionary Computing · Computer Science 2024-08-23 Frank Neumann , Carsten Witt

We present a data structure called a history graph that offers a practical basis for the analysis of genome evolution. It conceptually simplifies the study of parsimonious evolutionary histories by representing both substitutions and double…

Genomics · Quantitative Biology 2014-05-13 Benedict Paten , Daniel R. Zerbino , Glenn Hickey , David Haussler

The choice of crossover and mutation strategies plays a crucial role in the searchability, convergence efficiency and precision of genetic algorithms. In this paper, a novel improved genetic algorithm is proposed by improving the crossover…

Neural and Evolutionary Computing · Computer Science 2022-10-12 Dingming Yang , Zeyu Yu , Hongqiang Yuan , Yanrong Cui

Ancestral sequence reconstruction is a key task in computational biology. It consists in inferring a molecular sequence at an ancestral species of a known phylogeny, given descendant sequences at the tip of the tree. In addition to its many…

Populations and Evolution · Quantitative Biology 2022-07-27 Brandon Legried , Sebastien Roch

Reconciling gene trees with a species tree is a fundamental problem to understand the evolution of gene families. Many existing approaches reconcile each gene tree independently. However, it is well-known that the evolution of gene families…

Populations and Evolution · Quantitative Biology 2018-06-12 Riccardo Dondi , Manuel Lafond , Celine Scornavacca

The weighted ancestor problem on a rooted node-weighted tree $T$ is a generalization of the classic predecessor problem: construct a data structure for a set of integers that supports fast predecessor queries. Both problems are known to…

Data Structures and Algorithms · Computer Science 2024-05-24 Philip Bille , Yakov Nekrich , Solon P. Pissis

Genome rearrangement is a common model for molecular evolution. In this paper, we consider the Pairwise Rearrangement problem, which takes as input two genomes and asks for the number of minimum-length sequences of permissible operations…

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability…

Neural and Evolutionary Computing · Computer Science 2021-10-28 Xubin Wang , Yunhe Wang , Ka-Chun Wong , Xiangtao Li

Introduction It has been demonstrated that a pathway-based feature selection method which incorporates biological information within pathways into the process of feature selection usually outperform a gene-based feature selection algorithm…

Methodology · Statistics 2016-05-13 Suyan Tian , Howard H. Chang , Chi Wang
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