Learnable Evolution Model
Encyclopedia
The Learnable Evolution Model (LEM) is a novel, non-Darwinian methodology for evolutionary computation
that employs machine learning
to guide the generation of new individuals (candidate problem solution
s). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutation
s and/or recombination
s), LEM employs hypothesis generation and instantiation operators.
The hypothesis generation operator applies a machine learning program to induce descriptions that distinguish between high-fitness
and low-fitness individuals in each consecutive population
. Such descriptions delineate areas in the search space
that most likely contain the desirable solutions. Subsequently the instantiation operator samples these areas to create new individuals.
Evolutionary computation
In computer science, evolutionary computation is a subfield of artificial intelligence that involves combinatorial optimization problems....
that employs machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...
to guide the generation of new individuals (candidate problem solution
Candidate solution
In optimization , a candidate solution is a member of a set of possible solutions to a given problem. A candidate solution does not have to be a likely or reasonable solution to the problem – it is simply in the set that satisfies all constraints.The space of all candidate solutions is called the...
s). Unlike standard, Darwinian-type evolutionary computation methods that use random or semi-random operators for generating new individuals (such as mutation
Mutation (genetic algorithm)
In genetic algorithms of computing, mutation is a genetic operator used to maintain genetic diversity from one generation of a population of algorithm chromosomes to the next...
s and/or recombination
Genetic recombination
Genetic recombination is a process by which a molecule of nucleic acid is broken and then joined to a different one. Recombination can occur between similar molecules of DNA, as in homologous recombination, or dissimilar molecules, as in non-homologous end joining. Recombination is a common method...
s), LEM employs hypothesis generation and instantiation operators.
The hypothesis generation operator applies a machine learning program to induce descriptions that distinguish between high-fitness
Fitness (biology)
Fitness is a central idea in evolutionary theory. It can be defined either with respect to a genotype or to a phenotype in a given environment...
and low-fitness individuals in each consecutive population
Population
A population is all the organisms that both belong to the same group or species and live in the same geographical area. The area that is used to define a sexual population is such that inter-breeding is possible between any pair within the area and more probable than cross-breeding with individuals...
. Such descriptions delineate areas in the search space
Search space
Search space may refer to one of the following.*In optimization, the domain of the function to be optimized*In search algorithms of computer science, the set of all possible solutions...
that most likely contain the desirable solutions. Subsequently the instantiation operator samples these areas to create new individuals.