Machine learning enables computers to imitate human behaviour by teaching them historical data and information about possible future events. This section will discuss fascinating machine learning techniques, such as Gene expression programming, linkage disequilibrium score regression, and cultural algorithm.

Gene expression programming

Gene expression programming (GEP) is an evolutionary approach that generates computer programmes or models. These computer programmes are intricate tree structures that learn and adapt by altering their sizes, forms, and composition, similar to a live thing. Similarly to live organisms, GEP computer programmes are encoded in simple, fixed-length linear chromosomes. Thus, GEP is a genotype-phenotype system with an essential genome to store and transmit genetic information and a sophisticated phenotype to explore and adapt to the environment.

GEP is a member of the evolutionary algorithm family closely connected to genetic programming and algorithms. It inherits the linear chromosomes of fixed length from genetic algorithms and the expressive parse trees of various sizes and shapes from genetic programming.

In creating a genotype/phenotype system, gene expression programming uses linear chromosomes as the genotype and parse trees as the phenotype. This genotype/phenotype system is multigenic. Hence each chromosome encodes numerous parse trees. Thus, the computer programmes generated by GEP consist of many parse trees. Because these parse trees emerge from gene expression, they are known as expression trees in GEP.

Linkage disequilibrium score regression

Linkage disequilibrium score regression (LDSR or LDSC) uses summary statistics from genome-wide association studies (GWASs). It assesses polygenic effects and confounding factors like population stratification. Regression study of linkage disequilibrium scores and GWAS SNP test statistics.

LDSC can assess SNP-based heritability, partition it into groups, and calculate genetic correlations between variables. Since the LDSC method depends solely on summary statistics from an entire GWAS, We can utilise it effectively with high sample sizes. In LDSC, genetic correlations are computed based on the discrepancy between chi-square statistics and the expected value under the null hypothesis. LDSC can also be used to evaluate genetic connections across traits. This extension of LDSC, known as cross-trait LD score regression, is not biased when applied to overlap datasets.

Cultural algorithm

Cultural algorithms (CA) are a subfield of evolutionary computation in which, in addition to the population component, there is a knowledge component known as the belief space. Cultural algorithms can thus be viewed as an extension of a traditional genetic algorithm. A cultural algorithm divides the belief space into discrete categories. These categories indicate distinct domains of search space knowledge held by the population.

After each iteration, the belief space is updated by the most intelligent members of the population. Similar to genetic algorithms, a fitness function that evaluates we can use the performance of each person in a population to choose the best people.

Furthermore, cultural algorithms require a population-belief space interface. By utilising the update function, the most intelligent members of a population can modify the belief space. Moreover, the knowledge categories in the belief space can influence the population component through the influence function. By changing the DNA or the activities of individuals, the impact function can affect a population.