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In computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation,^{[1]} a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.
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. Techniques from evolutionary algorithms applied to the modeling of biological evolution are generally limited to explorations of microevolutionary processes and planning models based upon cellular processes. In most real applications of EAs, computational complexity is a prohibiting factor.^{[2]} In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems;^{[3]}^{[4]}^{[5]} therefore, there may be no direct link between algorithm complexity and problem complexity.
Evolutionary algorithms can be seen as a kind of Monte-Carlo method.^{[6]}
The following is an example of a generic single-objective genetic algorithm.
Step One: Generate the initial population of individuals randomly. (First generation)
Step Two: Repeat the following regenerational steps until termination:
Similar techniques differ in genetic representation and other implementation details, and the nature of the particular applied problem.
The following theoretical principles apply to all or almost all EAs.
The no free lunch theorem of optimization states that all optimization strategies are equally effective when the set of all optimization problems is considered. Under the same condition, no evolutionary algorithm is fundamentally better than another. This can only be the case if the set of all problems is restricted. This is exactly what is inevitably done in practice. 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). Thus, if two EAs are compared, this constraint is implied. 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.^{[14]}^{[15]} Another possibility to tailor an EA to a given problem domain is to involve suitable heuristics, local search procedures or other problem-related procedures in the process of generating the offspring. This form of extension of an EA is also known as a memetic algorithm. Both extensions play a major role in practical applications, as they can speed up the search process and make it more robust.^{[14]}^{[16]}
Further information: Convergence (evolutionary computing) |
See also: Convergent evolution |
For EAs in which, in addition to the offspring, at least the best individual of the parent generation is used to form the subsequent generation (so-called elitist EAs), there is a general proof of convergence under the condition that an optimum exists. 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 an improvement of the fitness of the respective best individual will occur with a probability . Thus:
I.e., the fitness values represent a monotonically non-decreasing sequence, which is bounded due to the existence of the optimum. From this follows the convergence of the sequence against the optimum.
Since the proof makes no statement about the speed of convergence, it is of little help in practical applications of EAs. But it does justify the recommendation to use elitist EAs. However, when using the usual panmictic population model, elitist EAs tend to converge prematurely more than non-elitist ones.^{[17]} In a panmictic population model, mate selection (step 2 of the section about implementation) is such that every individual in the entire population is eligible as a mate. 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.^{[18]}^{[19]}
With the theory of virtual alphabets, David E. Goldberg showed in 1990 that by using a representation with real numbers, an EA that uses classical recombination operators (e.g. uniform or n-point crossover) cannot reach certain areas of the search space, in contrast to a coding with binary numbers.^{[20]} This results in the recommendation for EAs with real representation to use arithmetic operators for recombination (e.g. arithmetic mean or intermediate recombination). With suitable operators, real-valued representations are more effective than binary ones, contrary to earlier opinion.^{[21]}^{[22]}
A possible limitation^{[according to whom?]} of many evolutionary algorithms is their lack of a clear genotype–phenotype distinction. In nature, the fertilized egg cell undergoes a complex process known as embryogenesis to become a mature phenotype. 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.^{[23]}^{[24]} Such indirect (also known as generative or developmental) encodings also enable evolution to exploit the regularity in the environment.^{[25]} Recent work in the field of artificial embryogeny, or artificial developmental systems, seeks to address these concerns. And gene expression programming successfully explores a genotype–phenotype system, where the genotype consists of linear multigenic chromosomes of fixed length and the phenotype consists of multiple expression trees or computer programs of different sizes and shapes.^{[26]}^{[improper synthesis?]}
The areas in which evolutionary algorithms are practically used are almost unlimited^{[5]} and range from industry,^{[27]}^{[28]} engineering,^{[2]}^{[3]}^{[29]} complex scheduling,^{[4]}^{[30]}^{[31]} agriculture,^{[32]} robot movement planning^{[33]} and finance^{[34]}^{[35]} to research^{[36]}^{[37]} 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. For example, if peak utilisation of resources such as personnel deployment or energy consumption is to be avoided in a scheduling task, it is not sufficient to assess the maximum utilisation. 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.^{[38]} There are therefore some publications that are aimed at the beginner and want to help avoiding beginner's mistakes as well as leading an application project to success.^{[38]}^{[39]}^{[40]} This includes clarifying the fundamental question of when an EA should be used to solve a problem and when it is better not to.
Swarm algorithms^{[clarification needed]} include:
In 2020, Google stated that their AutoML-Zero can successfully rediscover classic algorithms such as the concept of neural networks.^{[46]}
The computer simulations Tierra and Avida attempt to model macroevolutionary dynamics.
^{[47]}^{[48]}^{[49]}