Evolutionary Algorithm

Genetic programming

In Computer Science, especially in artificial intelligence, evolutionary algorithms (EA) are a kind of algorithm that simulate evolution to optimize something.

Each generation of solution is subjected to some kind of fitness function; those that survive are then recombined in some way to make the next generation of solution. This is done until a certain level of fitness is reached, or a determined number of generations have been used.

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 environment within which the solutions ‘live.’ Evolution of the population then takes place after the repeated application of the above operators. ‘Artificial evolution’ describes a process involving individual evolutionary algorithms. 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; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics, and chemistry

In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to evaluation of fitness function (how close a given design solution is to achieving the set aims). 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.

A possible limitation 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 (aka 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.

One EA, Gene expression programming (GEP), does explore a genotype-phenotype system. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype-phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it.

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