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. 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; therefore, there may be no direct link between algorithm complexity and problem complexity.
Evaluate the individual fitness of new individuals.
Replace least-fit population with new individuals.
Similar techniques differ in genetic representation and other implementation details, and the nature of the particular applied problem.
Genetic algorithm - This is the most popular type of EA. One seeks the solution of a problem in the form of strings of numbers (traditionally binary, although the best representations are usually those that reflect something about the problem being solved), by applying operators such as recombination and mutation (sometimes one, sometimes both). This type of EA is often used in optimization problems. Another name for it is fetura, from the Latin for breeding.
Genetic programming - Here the solutions are in the form of computer programs, and their fitness is determined by their ability to solve a computational problem.
Evolutionary programming - Similar to genetic programming, but the structure of the program is fixed and its numerical parameters are allowed to evolve.
Gene expression programming - Like genetic programming, GEP also evolves computer programs but it explores a genotype-phenotype system, where computer programs of different sizes are encoded in linear chromosomes of fixed length.
Evolution strategy - Works with vectors of real numbers as representations of solutions, and typically uses self-adaptive mutation rates.
Neuroevolution - Similar to genetic programming but the genomes represent artificial neural networks by describing structure and connection weights. The genome encoding can be direct or indirect.
Learning classifier system - Here the solution is a set of classifiers (rules or conditions). A Michigan-LCS evolves at the level of individual classifiers whereas a Pittsburgh-LCS uses populations of classifier-sets. Initially, classifiers were only binary, but now include real, neural net, or S-expression types. Fitness is typically determined with either a strength or accuracy based reinforcement learning or supervised learning approach.
Comparison to biological processes
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. Such indirect (a.k.a. generative or developmental) encodings also enable evolution to exploit the regularity in the environment. 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.[improper synthesis?]
The runner-root algorithm (RRA) is inspired by the function of runners and roots of plants in nature 
Artificial bee colony algorithm - Based on the honey bee foraging behaviour. Primarily proposed for numerical optimization and extended to solve combinatorial, constrained and multi-objective optimization problems.
Bees algorithm is based on the foraging behaviour of honey bees. It has been applied in many applications such as routing and scheduling.
Hunting Search - A method inspired by the group hunting of some animals such as wolves that organize their position to surround the prey, each of them relative to the position of the others and especially that of their leader. It is a continuous optimization method  adapted as a combinatorial optimization method.
Adaptive dimensional search - Unlike nature-inspired metaheuristic techniques, an adaptive dimensional search algorithm does not implement any metaphor as an underlying principle. Rather it uses a simple performance-oriented method, based on the update of the search dimensionality ratio (SDR) parameter at each iteration.
Firefly algorithm is inspired by the behavior of fireflies, attracting each other by flashing light. This is especially useful for multimodal optimization.
Harmony search - Based on the ideas of musicians' behavior in searching for better harmonies. This algorithm is suitable for combinatorial optimization as well as parameter optimization.
Memetic algorithm - A hybrid method, inspired by Richard Dawkins's notion of a meme, it commonly takes the form of a population-based algorithm coupled with individual learning procedures capable of performing local refinements. Emphasizes the exploitation of problem-specific knowledge, and tries to orchestrate local and global search in a synergistic way.
^F. Merrikh-Bayat, The runner-root algorithm: A metaheuristic for solving unimodal and multimodal optimization problems inspired by runners and roots of plants in nature, Applied Soft Computing, Vol. 33, pp. 292-303, 2015
Michalewicz Z., Fogel D.B. (2004). How To Solve It: Modern Heuristics, Springer.
Benk? A., Dósa G., Tuza Z. (2010), Bin Packing/Covering with Delivery, Solved with the Evolution of Algorithms, Proc. 2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010, pp. 298-302.
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