What are memetic algorithms used for?

What are memetic algorithms used for?

A memetic algorithm (MA) in computer science and operations research, is an extension of the traditional genetic algorithm. It may provide a sufficiently good solution to an optimization problem. It uses a local search technique to reduce the likelihood of premature convergence.

Who invented memetic algorithms?

3 A Multi-Objective Memetic Algorithm for Design Optimization. The term Memetic Algorithm was first introduced by Moscato (1989) to describe populationbased hybrid evolutionary algorithms (EA) which are coupled with local refinement strategies.

How does genetic programming work?

Genetic programming is a domain-independent method that genetically breeds a population of computer programs to solve a problem. Specifically, genetic programming iteratively transforms a population of computer programs into a new generation of programs by applying analogs of naturally occurring genetic operations.

What are the disadvantages of genetic algorithm?

Disadvantages of Genetic Algorithm GA implementation is still an art. GA requires less information about the problem, but designing an objective function and getting the representation and operators right can be difficult. GA is computationally expensive i.e. time-consuming.

Are genetic algorithms efficient?

In the attached paper (which is under review), it has been claimed that in spite of what is generally supposed, GA is not an efficient optimization tool; because, its main operator, mating (crossover), cannot operate properly in Epistatic problems.

How genetic programming is different from genetic algorithm?

The main difference between genetic programming and genetic algorithms is the representation of the solution. Genetic programming creates computer programs in the lisp or scheme computer languages as the solution. Genetic algorithms create a string of numbers that represent the solution.

What is genetic software?

In artificial intelligence, genetic programming (GP) is a technique of evolving programs, starting from a population of unfit (usually random) programs, fit for a particular task by applying operations analogous to natural genetic processes to the population of programs.