What is the difference between genetic algorithm and evolutionary algorithm?
In a “genetic algorithm,” the problem is encoded in a series of bit strings that are manipulated by the algorithm; in an “evolutionary algorithm,” the decision variables and problem functions are used directly.
What problems are genetic algorithms good for?
Problems which appear to be particularly appropriate for solution by genetic algorithms include timetabling and scheduling problems, and many scheduling software packages are based on GAs. GAs have also been applied to engineering.
What are the advantages of evolutionary optimization techniques?
Evolutionary algorithm optimizers are global optimization methods and scale well to higher dimensional problems. They are robust with respect to noisy evaluation functions, and the handling of evaluation functions which do not yield a sensible result in given period of time is straightforward.
What are the main steps required to solve any evolutionary algorithm?
An EA contains four overall steps: initialization, selection, genetic operators, and termination. These steps each correspond, roughly, to a particular facet of natural selection, and provide easy ways to modularize implementations of this algorithm category.
How are genetic algorithms different from evolution algorithms?
The main difference seems to be that a genetic algorithm represents a solution using a sequence of integers, whereas an evolution strategy uses a sequence of real numbers — reference: http://en.wikipedia.org/wiki/Evolutionary_algorithm#.
How are individuals selected in an evolution strategy?
In evolution strategies, the individuals are coded as vectors of real numbers. On reproduction, parents are selected randomly and the fittest offsprings are selected and inserted in the next generation. ES individuals are self-adapting.
How are genes coded in a genetic algorithm?
In genetic algorithms, the individuals are coded as integers. The selection is done by selecting parents proportional to their fitness. So individuals must be evaluated before the first selection is done.
How do genetic operators work on the bit level?
Genetic operators work on the bit-level (e.g. cutting a bit string into multiple pieces and interchange them with the pieces of the other parent or switching single bits). That’s the theory.