Genetic representationIn 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).
Genetic operatorA genetic operator is an operator used in evolutionary algorithms (EA) to guide the algorithm towards a solution to a given problem. There are three main types of operators (mutation, crossover and selection), which must work in conjunction with one another in order for the algorithm to be successful.Mutation (evolutionary algorithm)Mutation is a genetic operator used to maintain genetic diversity of the 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 genetic sequence will be flipped from its original state.Population model (evolutionary algorithm)The population model of an evolutionary algorithm (EA) describes the structural properties of its population to which its members are subject. A population is the set of all proposed solutions of an EA considered in one iteration, which are also called individuals according to the biological role model. The individuals of a population can generate further individuals as offspring with the help of the genetic operators of the procedure.Evolution strategyEvolution strategy (ES) from computer science is a subclass of evolutionary algorithms, which serves as an optimization technique. It uses the major genetic operators mutation, recombination and selection of parents.Evolutionary algorithmEvolutionary algorithms (EA) reproduce essential elements of biological evolution in a computer algorithm in order to solve "difficult" problems, at least approximately, for which no exact or satisfactory solution methods are known. They are metaheuristics and population-based bio-inspired algorithms and evolutionary computation, which itself are part of the field of computational intelligence.Crossover (evolutionary algorithm)Crossover in evolutionary algorithms and evolutionary computation, also called recombination, is a genetic operator used to combine the genetic information of two parents to generate new offspring. It is one way to stochastically generate new solutions from an existing population, and is analogous to the crossover that happens during sexual reproduction in biology. New solutions can also be generated by cloning an existing solution, which is analogous to asexual reproduction.
Causality conditionsCausality conditions are classifications of Lorentzian manifolds according to the types of causal structures they admit. In the study of spacetimes, there exists a hierarchy of causality conditions which are important in proving mathematical theorems about the global structure of such manifolds. These conditions were collected during the late 1970s. The weaker the causality condition on a spacetime, the more unphysical the spacetime is.
ParameterA parameter (from Ancient Greek (pará)'beside, subsidiary' and (métron)'measure'), generally, is any characteristic that can help in defining or classifying a particular system (meaning an event, project, object, situation, etc.).
PhenotypeIn genetics, the phenotype (from Ancient Greek (phaínō)'to appear, show' and (túpos)'mark, type') is the set of observable characteristics or traits of an organism. The term covers all traits of an organism other than its genome, however transitory: the organism's morphology (physical form and structure), its developmental processes, its biochemical and physiological properties whether reversible or irreversible, and all its behavior, such as a peacock's display.
String (computer science)Strings are typically made up of characters, and are often used to store human-readable data, such as words or sentences.In computer programming, a string is traditionally a sequence of characters, either as a literal constant or as some kind of variable. The latter may allow its elements to be mutated and the length changed, or it may be fixed (after creation).
Data structureIn computer science, a data structure is a way to organize and store data that is usually chosen for efficient access to data. More precisely, a data structure is the physical implementation of a data type, including specifications of the data organization and storage format, as well as functions or operations for working with this data. Data structures are closely related to abstract data types (ADTs).*As an Amazon Associate I earn from qualifying purchases.