Population: It is a subset of all the possible solutions to the given problem.
Chromosomes: A chromosome is the individual solution to the given problem.
Gene: A gene is one element position of a chromosome.
Allele: It is the value which a gene takes for a particular chromosome.
Genotype: Genotype is the population (solution) in the computation space that can be represented in a simple and easy-to-understand manner so that it can be easily manipulated using a computing system.
Phenotype: Phenotype is the population (solution) in the real-world solution space that can be represented in real world situations.
Fitness Function (or Evaluation Function): It is the function which accepts the solution as input and produces the suitability of the solution as the output.
Genetic Operators: Operators which change the genetic composition of the offspring are known as genetic operators. These include crossover, mutation, selection, etc.
Mutation: The process in which a new solution is obtained by introducing a small random tweak in the chromosome.
Parent selection: The process of selecting individuals from the population (or parents) which will mate and recombine to generate off-springs for the next generation.
Soft computing: A group of computational techniques that are based on artificial intelligence (AI) and natural selection. It is often used to develop cost-effective solutions for complex real-world problems that are either difficult or impossible to be solved by conventional computing techniques.
Transfer learning: The reuse of a pre-trained model on a new problem.
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