21 papers · ranked by Valyu relevance
Urmzd Mukhammadnaim
| AUTHOR: | Urmzd Mukhammadnaim | | |…
Mihai Oltean
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem…
Ting Hu, Gabriela Ochoa, Wolfgang Banzhaf
Genotype-to-phenotype mappings translate genotypic variations such as mutations into phenotypic changes. Neutrality is the observation that some mutations do not lead to phenotypic changes. Studying the search trajectories in genotypic and phenotypic spaces, especially through neutral mutations, helps us to better…
Mihai Oltean
A new model for evolving Evolutionary Algorithms (EAs) is proposed in this paper. The model is based on the Multi Expression Programming (MEP) technique. Each MEP chromosome encodes an evolutionary pattern which is repeatedly used for generating the individuals of a new generation. The evolved pattern is embedded into…
Léo Françoso Dal Piccol Sotto, Vinícius Veloso de Melo
Traditional Linear Genetic Programming (LGP) algorithms are based only on the selection mechanism to guide the search. Genetic operators combine or mutate random portions of the individuals, without knowing if the result will lead to a er individual. Probabilistic Model Building Genetic Programming (PMB-GP) methods…
Tomasz Praczyk, Maciej Szymkowiak
In the paper, a new evolutionary technique called Linear Matrix Genetic Programming (LMGP) is proposed. It is a matrix extension of Linear Genetic Programming and its application is data-driven black-box control-oriented modeling in conditions of limited access to training data. In LMGP, the model is in the form of an…
Mihai Oltean
| 1 | Introduction | | 13 | | --- | --- | --- | --- | | | 1.1 | Machine Learning and Genetic Programming | 13 | | | 1.2 | Thesis structure and achievements | 15 | | | 1.3 | Other ML results not included in this Thesis | 17 | | 2 | | Genetic Programming and related techniques | 18 | | | 2.1 | Genetic Programming | 18 |…
Mihai Oltean
We investigate the possibility of encoding multiple solutions of a problem in a single chromosome. The best solution encoded in an individual will represent (will provide the fitness of) that individual. In order to obtain some benefits the chromosome decoding process must have the same complexity as in the case of a…
Xu Zheng, Tianzhen Wang, Qunhao Niu, Jiayuan Wu + 5 more
'Huijiang Gao' 'Junya Li' 'Lingyang Xu' 'David G. Riley'] Simple Summary The effect of optimized mating methods for long-term selection has not been studied in cattle breeding. In this study, the linear programming and optimal contribution selection methods on the genetic gain and inbreeding level of beef cattle were…
Nicolas Scalzitti, Iliya Miralavy, David E. Korenchan, Christian T. Farrar + 2 more
GP algorithms are powerful evolutionary computing techniques, a branch of AI and are widely used in different fields, such as engineering or bioinformatics . GP is a stochastic algorithm (an extension of GA) inspired by the Darwinian evolution concepts and is useful for automatically solving complex optimization…
Arturo Chavoya, Cuauhtemoc Lopez-Martin, Irma R. Andalon-Garcia, M. E. Meda-Campaña + 1 more
'M. E. Meda-Campaña' 'Richard James Morris'] Statistical and genetic programming techniques have been used to predict the software development effort of large software projects. In this paper, a genetic programming model was used for predicting the effort required in individually developed projects. Accuracy obtained…
Mauro Castelli, Luca Manzoni, Aleš Popovič
Quality of service, that is, the waiting time that customers must endure in order to receive a service, is a critical performance aspect in private and public service organizations. Providing good service quality is particularly important in highly competitive sectors where similar services exist. In this paper…
Tanistha Nayak, Tirtharaj Dash
— Solving Quadratic equation is one of the intrinsic interests as it is the simplest nonlinear equations. A novel approach for solving Quadratic Equation based on Genetic Algorithms (GAs) is presented. Genetic Algorithms (GAs) are a technique to solve problems which need optimization. Generation of trial solutions have…
Hao Cheng, Keyu Xu, Kuruvilla Joseph Abraham
Low-cost genome-wide single-nucleotide polymorphisms (SNPs) are routinely used in animal breeding programs. Compared to SNP arrays, the use of whole-genome sequence data generated by the next-generation sequencing technologies (NGS) has great potential in livestock populations. However, a large number of animals are…
Fatima Shokor, Pascal Croiseau, Hugo Gangloff, Romain Saintilan + 3 more
Genomic prediction aims to predict the breeding values of multiple complex traits, usually assumed to be normally distributed by the largely used statistical methods, thus imposing linear genetic correlations between traits. While statistical methods are of great value for genomic prediction, these methods do not…
Alexander R. Bricco, Iliya Miralavy, Shaowei Bo, Or Perlman + 4 more
Proteins are used by scientists to serve a variety of purposes in clinical practice and laboratory research. To optimize proteins for greater function, a variety of techniques have been developed. For the development of reporter genes used in Magnetic Resonance Imaging (MRI) based on Chemical Exchange Saturation…
Stephen D Turner, Scott M Dudek, Marylyn D Ritchie
Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene…
Deniz Akdemir
Optimal subset selection is an important task that has numerous algorithms designed for it and has many application areas. STPGA contains a special genetic algorithm supplemented with a tabu memory property (that keeps track of previously tried solutions and their fitness for a number of iterations), and with a…
Steen Lysgaard, Paul C. Jennings, Jens Strabo Hummelshøj, Thomas Bligaard + 1 more
A machine learning (ML) model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in Genetic Algorithm (GA)-based approaches by using the ML model as a surrogate. This leads to a machine learning accelerated genetic algorithm (MLaGA) combining robust…
Pouya Ahadi, Balabhaskar Balasundaram, Juan S. Borrero, Charles Chen
In this study, we address the mate selection problem in the hybridization stage of a breeding pipeline, which constitutes the multi-objective breeding goal key to the performance of a variety development program. The solution framework we formulate seeks to ensure that individuals with the most desirable genomic…
Qianxiang Ai, Joshua Schrier
In a recent paper in this journal (Chem. Mater. 2022, 34, 2545-2552), Twyman et al. studied the environmental stability of crystals by introducing a greedy heuristic algorithm for determining possible oxidation reactions. We show how the problem can be solved exactly, with less code and comparable computational time by…