Introduction : When people mention the genetic algorithm (GA), they will associate Darwin's biological evolution theory. Genetic algorithm (GA) is a randomized search method that derives from the evolutionary laws of the biological world.
Today we focus on the genetic algorithm (GA). ^_^
When people mention the genetic algorithm (GA), they will associate Darwin's biological evolution theory. Genetic algorithm (GA) is a randomized search method that derives from the evolutionary laws of the biological world. It is made by J. Professor Holland first proposed in 1975. At present, genetic algorithm (GA) has become an important branch of evolutionary computation research.
Genetic algorithm (GeneTIcAlgorithm) is a computational model that simulates the natural evolutionary process of Darwin's biological evolution theory and the biological evolution process of genetics. It is a method to search for optimal solutions by simulating natural evolutionary processes.
The genetic algorithm (GA) begins with a population (populaTIon) that represents a potential solution set of problems, while a population consists of a certain number of individuals encoded by genes. Each individual is actually an entity with a characteristic chromosome. Chromosome as the main carrier of genetic material, that is, a collection of multiple genes, its internal representation (ie genotype) is a combination of genes that determines the external representation of the shape of the individual. Therefore, mapping from phenotype to genotype, that is, coding work, needs to be implemented at the beginning.
Because the work of imitating gene coding is very complicated, it is often simplified. After the first generation of populations are produced, according to the principle of survival of the fittest and the principle of survival of the fittest, generational (generaTIon) evolution produces more and better approximate solutions, in each generation, according to the problem domain. The individual's fitness size selects (selecTIon) individuals and performs crossover and mutation by means of genetic operators of natural genetics to produce a population representing the new solution set. This process will lead to a population of natural evolution like the descendant population is more adaptable to the environment than the previous generation, the best individual in the last generation population is decoded, which can be used as a problem to approximate the optimal solution.
Genetic manipulation is the practice of simulating the genetics of biological genes. In the genetic algorithm, after coding to form the initial population, the task of genetic manipulation is to apply certain operations to the individuals of the group according to their environmental fitness (adaptation assessment), so as to realize the evolution process of the survival of the fittest. From the perspective of optimizing search, genetic operations can solve the problem, optimize from generation to generation, and approximate the optimal solution.
Framework and terminology:1) Coding—The process of translating a parameter of a problem space into a genetic space by a chromosome or an individual composed of a certain structure. At present, several commonly used coding techniques include binary coding, floating point coding, character coding, and coding, and the most common one is binary coding. There are three specifications for evaluating coding strategies: a) completeness; b) soundness; c) nonredundancy.
2) Fitness function—represents an individual's ability to adapt to the environment and also indicates the individual's ability to breed offspring. The fitness function of the genetic algorithm, also called the evaluation function, is an indicator used to judge the pros and cons of an individual in a group. It is evaluated based on the objective function of the problem sought. Genetic algorithms generally do not require other external information in the process of search evolution, and only use the evaluation function to evaluate the merits of individuals or solutions, and as a basis for future genetic operations. The fitness function design directly affects the performance of the genetic algorithm, so the design of the fitness function needs to meet the following conditions: a) single value, continuous, non-negative, maximizing; b) reasonable and consistent; c) small amount of calculation; d ) versatility.
3) Initial population selection - Individuals in the initial population are randomly generated. The initial group can be set as follows: a) According to the inherent knowledge of the problem, try to grasp the distribution of the space occupied by the optimal solution in the whole problem space, and then set the initial group within the distribution range. b) Randomly generate a certain number of individuals, and then pick the best individuals from them to add to the initial population. This process is iterated until the number of individuals in the initial population reaches a predetermined scale.
4) Chromosomes - also known as genotypes, a certain number of individuals constitute a population, and the number of individuals in a group is called the size of the group.
5) Genes - Elements in a string, genes used to represent individual characteristics.
6) Gene position—referred to as a gene position, in which the position of a gene in a string is called the Gene Position.
7) Characteristic value - When a string is used to represent an integer, the characteristic value of the gene is identical to the weight of the binary number.
8) Choice—Select the winning individual from the group and eliminate the operation of the inferior individual. The selection operator is sometimes referred to as a reproduction operator. The purpose of the selection is to directly pass the optimized individual (or solution) to the next generation or to generate a new individual through pairing to regenerate to the next generation. The selection operation is based on the fitness assessment of the individual in the group. Currently used selection operators are: fitness ratio method, random traversal sampling method, partial selection method, tournament selection and roulette selection method (the simplest and most commonly used).
9) Intersection—The operation of replacing a partial structure of two parent individuals to generate a new individual. The central role of genetic algorithms is the crossover operator of genetic operations. The crossover operator randomly exchanges two individuals in the population according to the crossover rate, and is able to generate new combinations of genes, hoping to combine the beneficial genes together. Through crossover, the search ability of genetic algorithms has been greatly improved. The most common crossover operator is a one-point crossover.
10) The mutation-mutation operator is a change in the gene value at certain loci of individual strings in a population. The local random search ability of the mutation operator can accelerate the convergence to the optimal solution; the mutation operator can maintain the diversity of the group and prevent the immature convergence phenomenon. Depending on the individual coding representation, there may be: a) real value variation; b) binary variation. The selection of the mutation rate is generally affected by factors such as population size and chromosome length, and usually a small value is selected.
11) Termination conditions—The algorithm terminates when the optimal individual's fitness reaches a given threshold, or the optimal individual's fitness and group fitness no longer rise, or when the number of iterations reaches a preset algebra.
Genetic manipulation is carried out with efficient and directional search. Genetic manipulation involves three basic genetic operators: selection; crossover; mutation. The effect of genetic manipulation is closely related to the operational probability, coding method, population size, initial population, and fitness function settings of the three genetic operators. The role of the three basic genetic operators: a) the role of selection: survival of the fittest, survival of the fittest; b) the role of crossover: to ensure the stability of the population, to the direction of the optimal solution; c) the role of variation: ensure the population The diversity that avoids local convergence that may occur.
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