Evolutionary algorithm

Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape.

The following is an example of a generic evolutionary algorithm:[7][8][9] Similar techniques differ in genetic representation and other implementation details, and the nature of the particular applied problem.

Therefore, to improve an EA, it must exploit problem knowledge in some form (e.g. by choosing a certain mutation strength or a problem-adapted coding).

In addition, an EA can use problem specific knowledge by, for example, not randomly generating the entire start population, but creating some individuals through heuristics or other procedures.

Without loss of generality, a maximum search is assumed for the proof: From the property of elitist offspring acceptance and the existence of the optimum it follows that per generation

In non-panmictic populations, selection is suitably restricted, so that the dispersal speed of better individuals is reduced compared to panmictic ones.

Thus, the general risk of premature convergence of elitist EAs can be significantly reduced by suitable population models that restrict mate selection.

This indirect encoding is believed to make the genetic search more robust (i.e. reduce the probability of fatal mutations), and also may improve the evolvability of the organism.

An example of such tasks is the proverbial search for a needle in a haystack, e.g. in the form of a flat (hyper)plane with a single narrow peak.

The areas in which evolutionary algorithms are practically used are almost unlimited[6] and range from industry,[33][34] engineering,[3][4][35] complex scheduling,[5][36][37] agriculture,[38] robot movement planning[39] and finance[40][41] to research[42][43] and art.

The application of an evolutionary algorithm requires some rethinking from the inexperienced user, as the approach to a task using an EA is different from conventional exact methods and this is usually not part of the curriculum of engineers or other disciplines.

For example, the fitness calculation must not only formulate the goal but also support the evolutionary search process towards it, e.g. by rewarding improvements that do not yet lead to a better evaluation of the original quality criteria.

Rather, the number and duration of exceedances of a still acceptable level should also be recorded in order to reward reductions below the actual maximum peak value.

There are some other proven and widely used methods of nature inspired global search techniques such as In addition, many new nature-inspired or methaphor-guided algorithms have been proposed since the beginning of this century.