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Multi-Objective Optimization Concepts
Published in Yezid Donoso, Ramon Fabregat, Multi-Objective Optimization in Computer Networks Using Metaheuristics, 2016
Simulating the biological evolutionary process, evolutionary algorithms use a structure or individual to solve a problem. This representation is generally a bit chain or data structure that corresponds to the biological genotype. The genotype defines an individual organism that is expressed in a phenotype and is made up of one or several chromosomes, which in turn are made up of separate genes that take on certain values (alleles) of a genetic alphabet. A locus identifies a gene’s position in the chromosome. Hence, each individual codifies a set of parameters used as entry of the function under consideration. Finally, a set of chromosomes is called the population. Just as in nature, varied evolutionary operators function in the algorithm population trying to produce better individuals. The three operators most used in evolutionary algorithms are mutation, recombination, and selection.
Design and Simulation of Bio-Inspired Algorithm
Published in Mohammed Usman, Mohd Wajid, Mohd Dilshad Ansari, Enabling Technologies for Next Generation Wireless Communications, 2020
Sadaf Ajaz Khan, Javaid A. Sheikh, Tanzeela Ashraf, Mehboob-ul-Amin
Because this evolutionary algorithm has been derived from the natural process of evolution, quite a few metaphors related to that are used here. The following terms are used:Organism: The unit that is to be optimized (a radio parameter, a wireless resource etc.).Population: It consists of collection of possible solutions.Chromosome: It represents the string of binary symbols that are used to encode the solution for the problem under study. They are submitted to genetic operations.Fitness: In natural process the fit individuals pass on their characteristics to the next generation. It is a measure of how good a solution is.Gene: The components that make up the chromosomes.Allele: Alternative forms taken up by a gene.Locus: It is the location of a gene of interest on a chromosome.Mutation: It represents random changes to an individual from the current population to create children for the next generation.Selection: The process of choosing the most fit individuals so that they can send forward their genes to the later generation and weed out the weaker ones.
Neuro-Fuzzy–GA–AI Paradigms
Published in Jitendra R. Raol, Ajith K. Gopal, Mobile Intelligent Autonomous Systems, 2016
The long stretches of DNA that carry the genetic information of an individual (or a system) to build an organism are called chromosomes. In GA, the chromosomes represent encoding of information in a string of finite length and each chromosome consists of a string of bits (binary digit; 0 or 1), or it could be a symbol from a set of more than two elements; these strings could be real-valued numbers also. These chromosomes consist of genes and each gene represents a unit of information and it takes different values that are called alleles at different locations, which in turn are called loci. These strings which are composed of features or detectors, assume values like 0 or 1, which are located at different positions in the string. The total system is called the genotype or structure. The phenotype results when interaction of genotype with environment takes place. Thus, the GAs operate on population of possible sample solutions with chromosomes. The population members are known as individuals (meaning here a sample) and each sample individual is assigned a fitness value based on some objective function or cost function. Better solutions have higher fitness values and weaker solutions would have lower fitness values. In the initialization/reproduction phase, by randomly selecting information from the sample-search space and encoding it, a population of possible initial solutions is created. In the reproduction stage the individual strings are copied as per their fitness values. The strings with a greater fitness value are given higher probability of contributing one or more offspring to the next generation. In a crossover operation a site/location is selected randomly along the length of the chromosomes, and each chromosome is split into two pieces at the crossover site/location. The new samples are formed by joining the top piece of one chromosome with the tailpiece of the other. This crossover operation can be carried out in many different ways: it can be performed in the same string also, by swapping strings that are split at some location.
Hybrid evolutionary optimisation with learning for production scheduling: state-of-the-art survey on algorithms and applications
Published in International Journal of Production Research, 2018
Cheng and Gen (1994) combined a priority-based encoding method for creating an operation sequence. As we known, a gene in an individual is characterised by two factors: locus, i.e. the position of the gene located within the structure of chromosome, and allele, i.e. the value the gene takes. In this encoding method, the position of a gene is used to represent operation ID and its value is used to represent the priority of the operation for constructing a schedule among candidates. A schedule can be uniquely determined from this encoding. Gen, Tsujimura, and Kubota (1994) applied the indirect approach in genetic representation and a partial schedule of exchanging crossover operation for solving effectively job-shop scheduling problems.
Transhumanist Genetic Enhancement: Creation of a ‘New Man’ Through Technological Innovation
Published in The New Bioethics, 2021
Genome-wide association methods assume that: (i) non-random associations of alleles (single copies of a gene) at different loci in a given population (linkage disequilibrium) would enable one or few SNP to act as surrogate markers for the association(s), and (ii) these markers would be placed near to genetic variant(s) causing trait change(s). These assumptions are statistical and pinpointing causal mutations in subsequent fine-mapping studies remains a challenge (Wang et al.2010). A common difficulty is that significant SNP resulting from these analyses fall in non-coding regions of the DNA that may regulate gene expression even though the actual regulated gene is unknown.
Genetic variants within the COL5A1 gene are associated with ligament injuries in physically active populations from Australia, South Africa, and Japan
Published in European Journal of Sport Science, 2023
Javier Alvarez-Romero, Mary-Jessica N. Laguette, Kirsten Seale, Macsue Jacques, Sarah Voisin, Danielle Hiam, Julian A. Feller, Oren Tirosh, Eri Miyamoto-Mikami, Hiroshi Kumagai, Naoki Kikuchi, Nobuhiro Kamiya, Noriyuki Fuku, Malcolm Collins, Alison V. September, Nir Eynon
Although our sample size was relatively large compared with previous studies (Pabalan, Tharabenjasin, Phababpha, & Jarjanazi, 2018), some limitations may have narrowed the scope of our study. Firstly, correction for multiple testing was not included. However, an a priori hypothesis provided the rationale for all our statistical tests, as there are previous associations for the chosen genetic variants in connective tissue (Abrahams et al., 2014; Rahim et al., 2019). Differences in the allele and genotype frequencies distributions were noted between the three cohorts and therefore the data generated could not be combined for this specific genetic locus. These frequency differences were supported by the data available on the minor allele frequencies of the variants for the general European (rs12722 C = 41%; rs10628678 del allele = 27%) and East Asian descent population (rs12722 C = 78%; rs10628678 del allele = 53%) according to the 1000 genome project (NCBI database). The second finding of this study may indicate that the association observed in the SA cohort was a false positive error (Type I error), a limitation of traditional single-locus genetic associations, or a population-specific relationship exists between the variant and/or this region of the COL5A1 gene and risk of ACL injury. Population association differences may result from differences in genetic architecture related to differences in linkage disequilibrium between genetic variants at a given locus. It may be that the rs12722 is linked to a potential causal genetic variant contributing to the ACL rupture injury, which is not being noted in the other cohorts. Genetic association studies using DNA variants should therefore be explored in a multitude of populations in combination with the functional exploration of the expression of these loci within tissue samples of the various populations. In addition, the pooling of the AUS and SA cohorts with seemingly the same ancestral background, but possibly distinct population stratifications, may insert unknown biases that require further interrogation and refining. Similarly, the sports participation history between participants may influence the results. The JPN ACL rupture may represent another Type 1 error due to the small sample size (n = 64), which reduced the power of the analysis, and the results should be interpreted with caution. Furthermore, this study was not matched for case and control numbers between the three cohorts, creating an imbalance between groups during analysis. Moreover, the association of variants with all ligament injuries was only possible in JPN. Lastly, the information on the type of sports played was not available for all participants, excluding an important factor that influences the predisposition of ligament injuries, especially in non-contact injury events.