Genetic Algorithms (GAs)
- 1960s, Rechenberg (1965, 1973) introduced "evolution strategies", a method he used to optimize realâ'valued parameters for devices such as airfoils.
- This idea was further developed by Schwefel (1975, 1977). The field of evolution strategies has remained an active area of research, mostly developing independently from the field of genetic algorithms Fogel, Owens, and Walsh (1966) developed "evolutionary programming," a technique in which candidate solutions to given tasks were represented as finiteâ'state machines, which were evolved by randomly mutating their stateâ'transition diagrams and selecting the fittest.
- Together, evolution strategies, evolutionary programming, and genetic algorithms form the backbone of the field of evolutionary computation.
- Genetic algorithms (GAs) were invented by John Holland in the 1960s and were developed by Holland and his students and colleagues at the University of Michigan in the 1960s and the 1970s. In contrast with evolution strategies and evolutionary programming, Holland's original goal was not to design algorithms to solve specific problems, but rather to formally study the phenomenon of adaptation as it occurs in nature and to develop ways in which the mechanisms of natural adaptation might be imported into computer systems. Holland's 1975 book Adaptation in Natural and Artificial Systems presented the genetic algorithm as an abstraction of biological evolution and gave a theoretical framework for adaptation under the GA. Holland's GA is a method for moving from one population of "chromosomes" (e.g., strings of ones and zeros, or "bits") to a new population by using a kind of "natural selection" together with the geneticsâ'inspired operators of crossover, mutation, and inversion. Each chromosome consists of "genes" (e.g., bits), each gene being an instance of a particular "allele" (e.g., 0 or 1). The selection operator chooses those chromosomes in the...