Genetic algorithms belong to the family of evolutionary algorithms. Their goal is to obtain an approximate solution to a problem of optimization, where there is no exact method (or the solution is unknown) to solve in a reasonable time. Genetic algorithms use the concept of natural selection and apply it to a population of potential solutions to the given problem. The solution is approximated by successive “jumps,” as in a process of branch and bound, except that it is formulas that are searched and not directly values.
The use of genetic algorithms in solving problems is originally based on research of John Holland and his colleagues and students at the University of Michigan who, from 1960, worked on this subject. The novelty introduced by this group of researchers was the inclusion of the operator of crossover in addition to mutations. It is what enables the operator to more often closer to the optimum of a function by combining the genes in different individuals of the population. The first outcome of this research was the publication in 1975 of the Adaptation in Natural and Artificial System.
Students preparing their research papers on genetic algorithms have to understand that the popularization of genetic algorithms was due to the work of David Goldberg through his book Genetic Algorithms in Search, Optimization, and Machine Learning (1989).
This book is still published today. In Europe, the first conference on this type of subject was the European Conference on Artificial Life in 1991 (it celebrated its 20th anniversary in 2011), co-organized by Francisco Varela and Paul Bourgine.
Genetic, algorithms to enable the resolution of problems is based on different principles. The theoretical problem of convergence has been resolved by Raphael Cerf, based on the theory of stochastic disturbances Friedlin Weinzel in dynamic systems. Demonstration of R. Cerf also shows that the convergence process depends mainly on the mutation, the crossing can be eliminated in theory. However, the theoretical proof of convergence is of little use in practice, where the crossover operator is often the richness of the genetic algorithm with respect to methods like simulated annealing.
Overall, one starts with a base population that consists mostly of character strings each corresponding to a chromosome.
Genetic algorithms are based on biological phenomena and a few terms of genetics should be remembered.
Living organisms are composed of cells whose nuclei comprise chromosomes which are strings of DNA. The base of these chromosomes (the character of the DNA chain) is a gene. On each of these chromosomes, a suite of genes is a string that encodes the features of the body (e.g., eye color). The position of a gene on the chromosome is the locus. All the genes of an individual are its genotype and the entire gene pool of a species is the genome. The different versions of the same gene are called alleles.
Also used in genetic algorithms, an analogy with the theory of evolution, which proposes that over time, stored in a given population genes are those that are most suited to the needs of the species vis-à-vis the environment.
Research papers on genetic algorithms can be of great help for those who write their first research proposal on the topic.
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