/genetic-algo

Simple Genetic Algorithms implementation

Primary LanguageJupyter Notebook

Genetic Algorithm

Evolutionary Algorithms (EA)

generation based:

steady state: adds and deletes individuals to current population Standard selection schemes (STD):

  • porportionate
  • truncation
  • ranking
  • tournament
  • Boltzmann's (less known)

All have the property and goal to evolve the population toward higher fitness.

Problems:

  • multiple parameters, constant or dinnamic...
  • premature convergence
  • no convergence To research:

-multiobjective evolutionary algorithms

https://www.ime.usp.br/~rvicente/research.html

@article{DBLP:journals/corr/abs-cs-0610126, author = {Marcus Hutter and Shane Legg}, title = {Fitness Uniform Optimization}, journal = {CoRR}, volume = {abs/cs/0610126}, year = {2006}, url = {http://arxiv.org/abs/cs/0610126}, eprinttype = {arXiv}, eprint = {cs/0610126}, timestamp = {Mon, 13 Aug 2018 16:48:32 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-cs-0610126.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }

Glossary

Allele

Chromosome

Individual

Population

Generation

Fitness

Mating/Crossover

Mutation