|
Evolutionary Algorithm
Evolutionary algorithms (EA) reproduce essential elements of the biological evolution in a computer algorithm in order to solve "difficult" problems, at least Approximation, approximately, for which no exact or satisfactory solution methods are known. They belong to the class of Metaheuristic, metaheuristics and are a subset of Population Based Bio-Inspired Algorithms, population based bio-inspired algorithms and evolutionary computation, which itself are part of the field of computational intelligence. The mechanisms of biological evolution that an EA mainly imitates are reproduction, mutation, genetic recombination, recombination and natural selection, selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators. Evolutionary algorithms often perfor ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Biological Evolution
Evolution is the change in the heritable characteristics of biological populations over successive generations. It occurs when evolutionary processes such as natural selection and genetic drift act on genetic variation, resulting in certain characteristics becoming more or less common within a population over successive generations. The process of evolution has given rise to biodiversity at every level of biological organisation. The scientific theory of evolution by natural selection was conceived independently by two British naturalists, Charles Darwin and Alfred Russel Wallace, in the mid-19th century as an explanation for why organisms are adapted to their physical and biological environments. The theory was first set out in detail in Darwin's book ''On the Origin of Species''. Evolution by natural selection is established by observable facts about living organisms: (1) more offspring are often produced than can possibly survive; (2) traits vary among individuals with ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Landscape
Fitness may refer to: * Physical fitness, a state of health and well-being of the body * Fitness culture, a sociocultural phenomenon surrounding exercise and physical fitness * Fitness (biology), an individual's ability to propagate its genes * Fitness (cereal), a brand of breakfast cereals and granola bars * ''Fitness'' (magazine), a women's magazine, focusing on health and exercise * Fitness and figure competition, a form of physique training, related to bodybuilding * Fitness approximation, a method of function optimization evolutionary computation or artificial evolution methodologies * Fitness function, a particular type of objective function in mathematics and computer science * "Fitness", a 2018 song by Lizzo See also * FitNesse, a web server, a wiki, and a software testing tool *Survival of the fittest "Survival of the fittest" is a phrase that originated from Darwinian evolutionary theory as a way of describing the mechanism of natural selection. The biol ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Optimization (mathematics)
Mathematical optimization (alternatively spelled ''optimisation'') or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries. In the more general approach, an optimization problem consists of maxima and minima, maximizing or minimizing a Function of a real variable, real function by systematically choosing Argument of a function, input values from within an allowed set and computing the Value (mathematics), value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. Optimization problems Opti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Algorithm
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems via biologically inspired operators such as selection, crossover, and mutation. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, and causal inference. Methodology Optimization problems In a genetic algorithm, a population of candidate solutions (called individuals, creatures, organisms, or phenotypes) to an optimization problem is evolved toward better solutions. Each candidate solution has a set of properties (its chromosomes or genotype) which can be mutated and altered; traditionally, solutions are represented in binary as strings of 0s and 1s, but other encod ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Genetic Representation
In computer programming, genetic representation is a way of presenting solutions/individuals in evolutionary computation methods. The term encompasses both the concrete data structures and data types used to realize the genetic material of the candidate solutions in the form of a genome, and the relationships between search space and problem space. In the simplest case, the search space corresponds to the problem space (direct representation). The choice of problem representation is tied to the choice of genetic operators, both of which have a decisive effect on the efficiency of the optimization. Genetic representation can encode appearance, behavior, physical qualities of individuals. Difference in genetic representations is one of the major criteria drawing a line between known classes of evolutionary computation. Terminology is often analogous with natural genetics. The block of computer memory that represents one candidate solution is called an individual. The data in that blo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Offspring
In biology, offspring are the young creation of living organisms, produced either by sexual reproduction, sexual or asexual reproduction. Collective offspring may be known as a brood or progeny. This can refer to a set of simultaneous offspring, such as the chick (young bird), chicks hatched from one clutch (eggs), clutch of eggs, or to all offspring produced over time, as with the brood (honeybee), honeybee. Offspring can occur after mating, artificial insemination, or as a result of cloning. Human offspring (lineal descendant, descendants) are referred to as children; male children are sons and female children are daughters (see Kinship). Overview Offspring contains many parts and properties that are precise and accurate in what they consist of, and what they define. As the offspring of a new species, also known as a child or f1 generation, consist of genes of the father and the mother, which is also known as the parent generation. Each of these offspring contains numerous ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Mutation (evolutionary Algorithm)
Mutation is a genetic operator used to maintain genetic diversity of the chromosome (genetic algorithm), chromosomes of a population of an evolutionary algorithm (EA), including genetic algorithms in particular. It is analogous to biological mutation. The classic example of a mutation operator of a binary coded genetic algorithm (GA) involves a probability that an arbitrary bit in a genome (genetic algorithm), genetic sequence will be flipped from its original state. A common method of implementing the mutation operator involves generating a random variable for each bit in a sequence. This random variable tells whether or not a particular bit will be flipped. This mutation procedure, based on the biological point mutation, is called single point mutation. Other types of mutation operators are commonly used for representations other than binary, such as floating-point encodings or representations for combinatorial problems. The purpose of mutation in EAs is to introduce diversity in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Reproduce
Reproduction (or procreation or breeding) is the biological process by which new individual organisms – "offspring" – are produced from their "parent" or parents. There are two forms of reproduction: asexual and sexual. In asexual reproduction, an organism can reproduce without the involvement of another organism. Asexual reproduction is not limited to single-celled organisms. The cloning of an organism is a form of asexual reproduction. By asexual reproduction, an organism creates a genetically similar or identical copy of itself. The evolution of sexual reproduction is a major puzzle for biologists. The two-fold cost of sexual reproduction is that only 50% of organisms reproduce and organisms only pass on 50% of their genes.John Maynard Smith ''The Evolution of Sex'' 1978. Sexual reproduction typically requires the sexual interaction of two specialized reproductive cells, called gametes, which contain half the number of chromosomes of normal cells and are created by me ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Crossover (evolutionary Algorithm)
Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the chromosome (genetic algorithm), genetic information of two parents to generate new offspring. It is one way to stochastic, stochastically generate new candidate solution, solutions from an existing population, and is analogous to the chromosomal crossover, crossover that happens during sexual reproduction in biology. New solutions can also be generated by cloning (programming), cloning an existing solution, which is analogous to asexual reproduction. Newly generated solutions may be mutation (genetic algorithm), mutated before being added to the population. The aim of recombination is to transfer good characteristics from two different parents to one child. Different algorithms in evolutionary computation may use different data structures to store genetic information, and each genetic representation can be recombined with different crossover operat ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Selection (evolutionary Algorithm)
Selection is a genetic operator in an evolutionary algorithm (EA). An EA is a metaheuristic inspired by biological evolution and aims to solve challenging problems at least approximately. Selection has a dual purpose: on the one hand, it can choose individual genomes from a population for subsequent breeding (e.g., using the crossover operator). In addition, selection mechanisms are also used to choose candidate solutions (individuals) for the next generation. The biological model is natural selection. Retaining the best individual(s) of one generation unchanged in the next generation is called ''elitism'' or ''elitist selection''. It is a successful (slight) variant of the general process of constructing a new population. The basis for selection is the quality of an individual, which is determined by the fitness function. In memetic algorithms, an extension of EA, selection also takes place in the selection of those offspring that are to be improved with the help of a meme ( ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fitness Function
A fitness function is a particular type of objective or cost function that is used to summarize, as a single figure of merit, how close a given candidate solution is to achieving the set aims. It is an important component of evolutionary algorithms (EA), such as genetic programming, evolution strategies or genetic algorithms. An EA is a metaheuristic that reproduces the basic principles of biological evolution as a computer algorithm in order to solve challenging optimization or planning tasks, at least approximately. For this purpose, many candidate solutions are generated, which are evaluated using a fitness function in order to guide the evolutionary development towards the desired goal. Similar quality functions are also used in other metaheuristics, such as ant colony optimization or particle swarm optimization. In the field of EAs, each candidate solution, also called an ''individual'', is commonly represented as a string of numbers (referred to as a chromosome). A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Chromosome (evolutionary Algorithm)
A chromosome or genotype in evolutionary algorithms (EA) is a set of parameters which define a proposed solution of the problem that the evolutionary algorithm is trying to solve. The set of all solutions, also called ''individuals'' according to the biological model, is known as the ''population''. The genome of an individual consists of one, more rarely of several, chromosomes and corresponds to the genetic representation of the task to be solved. A chromosome is composed of a set of genes, where a gene consists of one or more semantically connected parameters, which are often also called ''decision variables''. They determine one or more phenotypic characteristics of the individual or at least have an influence on them. In the basic form of genetic algorithms, the chromosome is represented as a binary string, while in later variants and in EAs in general, a wide variety of other data structures are used. Chromosome design When creating the genetic representation of a task, it ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
