Efficiency Enhancement of Genetic Algorithms via Building-Block-Wise Fitness Estimation [PDF]
, 2004 This paper studies fitness inheritance as an efficiency enhancement technique for a class of competent genetic algorithms called estimation distribution algorithms. Probabilistic models of important sub-solutions are developed to estimate the fitness of a proportion of individuals in the population, thereby avoiding computationally expensive function ...
arxiv +1 more source
Epigenetics Algorithms: Self-Reinforcement-Attention mechanism to regulate chromosomes expression [PDF]
arXiv, 2023 Genetic algorithms are a well-known example of bio-inspired heuristic methods. They mimic natural selection by modeling several operators such as mutation, crossover, and selection. Recent discoveries about Epigenetics regulation processes that occur "on top of" or "in addition to" the genetic basis for inheritance involve changes that affect and ...
arxiv
Distributed Global Optimization (DGO) [PDF]
Proceedings of International Conference on Neural Networks
(ICNN'96) June 1996, 2020 A new technique of global optimization and its applications in particular to neural networks are presented. The algorithm is also compared to other global optimization algorithms such as Gradient descent (GD), Monte Carlo (MC), Genetic Algorithm (GA) and other commercial packages.
arxiv +1 more source
Intelligent modelling of bioprocesses: A comparison of structured and unstructured approaches [PDF]
, 2004 This contribution moves in the direction of answering some general questions about the most effective and useful ways of modelling bioprocesses. We investigate the characteristics of models that are good at extrapolating.
Baganz, F +5 more
core +1 more source
A Genetic Quantum Annealing Algorithm [PDF]
arXiv, 2022 A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA with input from quantum annealers. As in a classical GA, the algorithm works by breeding a population of possible solutions based on their fitness.
arxiv
Evolutionary L∞ identification and model reduction for robust control [PDF]
, 2000 An evolutionary approach for modern robust control oriented system identification and model reduction in the frequency domain is proposed. The technique provides both an optimized nominal model and a 'worst-case' additive or multiplicative uncertainty ...
Chiang R. Y. +7 more
core +1 more source
Ancestral haplotype reconstruction in endogamous populations using identity-by-descent.
PLoS Computational Biology, 2021 In this work we develop a novel algorithm for reconstructing the genomes of ancestral individuals, given genotype or sequence data from contemporary individuals and an extended pedigree of family relationships.
Kelly Finke +10 more
doaj +1 more source
Kernel Density Estimation by Genetic Algorithm [PDF]
arXiv, 2022 This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the ...
arxiv
Evolving dynamic multiple-objective optimization problems with objective replacement [PDF]
, 2005 This paper studies the strategies for multi-objective optimization in a dynamic environment. In particular, we focus on problems with objective replacement, where some objectives may be replaced with new objectives during evolution.
Chen, Q, Guan, SU, Mo, W
core
Systematic genetic analysis of the MHC region reveals mechanistic underpinnings of HLA type associations with disease. [PDF]
, 2019 The MHC region is highly associated with autoimmune and infectious diseases. Here we conduct an in-depth interrogation of associations between genetic variation, gene expression and disease.
Aguiar +76 more
core +2 more sources